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2,195 results on '"Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon)"'

1. The role of Monte Carlo simulation in understanding the performance of proton computed tomography

Author

Simon Rit, Jannis Dickmann, Sebastian Meyer, George Dedes, Reinhard W. Schulte, Nils Krah, R. P. Johnson, Vladimir Bashkirov, V. Giacometti, Katia Parodi, Guillaume Landry, Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU), Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), PRISME (PRISME), Institut de Physique des 2 Infinis de Lyon (IP2I Lyon), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Loma Linda University, University of California [Santa Cruz] (UCSC), and University of California

Subjects
Proton computed tomography, Scanner, Computer science, Physics::Medical Physics, Monte Carlo method, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Biophysics, Iterative reconstruction, 030218 nuclear medicine & medical imaging, Computational science, 03 medical and health sciences, 0302 clinical medicine, proton therapy, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Radiology, Nuclear Medicine and imaging, Tomography, Image resolution, Proton therapy, Monte Carlo simulation, proton computed tomography, Tomographic reconstruction, Radiological and Ultrasound Technology, Phantoms, Imaging, Detector, relative proton stopping power, Protons, Monte Carlo Method
Abstract

International audience; Proton computed tomography (pCT) is a promising tomographic imaging modality allowing direct reconstruction of proton relative stopping power (RSP) required for proton therapy dose calculation. In this review article, we aim at highlighting the role of Monte Carlo (MC) simulation in pCT studies. After describing the requirements for performing proton computed tomography and the various pCT scanners actively used in recent research projects, we present an overview of available MC simulation platforms. The use of MC simulations in the scope of investigations of image reconstruction, and for the evaluation of optimal RSP accuracy, precision and spatial resolution omitting detector effects is then described. In the final sections of the review article, we present specific applications of realistic Monte Carlo simulations of an existing pCT scanner prototype, which we describe in detail.

Published
2022

2. Improving motion‐mask segmentation in thoracic CT with multiplanar U‐nets

Author

Nicolas Pinon, Maciej Orkisz, Jean-Christophe Richard, Ludmilla Penarrubia, Eduardo Enrique Dávila Serrano, David Sarrut, Emmanuel Roux, Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de Réanimation Médicale, Hôpital de la Croix-Rousse [CHU - HCL], Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), and Imagerie Tomographique et Radiothérapie

Subjects
Lung Neoplasms, Computer science, Image registration, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Robustness (computer science), Carcinoma, Non-Small-Cell Lung, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Image Processing, Computer-Assisted, Humans, Segmentation, Generalizability theory, Computer vision, Four-Dimensional Computed Tomography, Artificial neural network, SARS-CoV-2, business.industry, Deep learning, Process (computing), COVID-19, General Medicine, 3. Good health, 030220 oncology & carcinogenesis, Memory footprint, Artificial intelligence, business
Abstract

International audience; Purpose. Motion-mask segmentation from thoracic computed tomography (CT) images is the process of extracting the region that encompasses lungs and viscera, where large displacements occur during breathing. It has been shown to help image registration between different respiratory phases. This registration step is, for example, useful for radiotherapy planning or calculating local lung ventilation. Knowing the location of motion discontinuity, that is, sliding motion near the pleura, allows a better control of the registration preventing unrealistic estimates. Nevertheless, existing methods for motion-mask segmentation are not robust enough to be used in clinical routine. This article shows that it is feasible to overcome this lack of robustness by using a lightweight deep-learning approach usable on a standard computer, and this even without data augmentation or advanced model design.Methods. A convolutional neural-network architecture with three 2D U-nets for the three main orientations (sagittal, coronal, axial) was proposed. Predictions generated by the three U-nets were combined by majority voting to provide a single 3D segmentation of the motion mask. The networks were trained on a database of nonsmall cell lung cancer 4D CT images of 43 patients. Training and evaluation were done with a K-fold cross-validation strategy. Evaluation was based on a visual grading by two experts according to the appropriateness of the segmented motion mask for the registration task, and on a comparison with motion masks obtained by a baseline method using level sets. A second database (76 CT images of patients with early-stage COVID-19), unseen during training, was used to assess the generalizability of the trained neural network.Results. The proposed approach outperformed the baseline method in terms of quality and robustness: the success rate increased from urn:x-wiley:00942405:media:mp15347:mp15347-math-0001 to urn:x-wiley:00942405:media:mp15347:mp15347-math-0002 without producing any failure. It also achieved a speed-up factor of 60 with GPU, or 17 with CPU. The memory footprint was low: less than 5 GB GPU RAM for training and less than 1 GB GPU RAM for inference. When evaluated on a dataset with images differing by several characteristics (CT device, pathology, and field of view), the proposed method improved the success rate from urn:x-wiley:00942405:media:mp15347:mp15347-math-0003 to urn:x-wiley:00942405:media:mp15347:mp15347-math-0004.Conclusion. With 5-s processing time on a mid-range GPU and success rates around urn:x-wiley:00942405:media:mp15347:mp15347-math-0005, the proposed approach seems fast and robust enough to be routinely used in clinical practice. The success rate can be further improved by incorporating more diversity in training data via data augmentation and additional annotated images from different scanners and diseases. The code and trained model are publicly available.

Published
2021

3. Learning 3D medical image keypoint descriptors with the triplet loss

Author

Sébastien Valette, Nicolas Loiseau–witon, Razmig Kéchichian, Adrien Bartoli, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Imagerie Tomographique et Radiothérapie, Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Institut Pascal (IP), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Valette, Sébastien, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and ANR-19-CE45-0015,TOPACS,Traitement Ouvert de données PACS(2019)

Subjects
Databases, Factual, Matching (graph theory), Computer science, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Biomedical Engineering, Health Informatics, 02 engineering and technology, Convolutional neural network, Descriptors, Synthetic data, Image (mathematics), Imaging, Three-Dimensional, Triplet loss, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 0202 electrical engineering, electronic engineering, information engineering, Medical imaging, Humans, Radiology, Nuclear Medicine and imaging, Convolution Neural Network, business.industry, Reproducibility of Results, 020207 software engineering, Pattern recognition, General Medicine, Keypoints, Computer Graphics and Computer-Aided Design, Computer Science Applications, High memory, ComputingMethodologies_PATTERNRECOGNITION, Benchmark (computing), 020201 artificial intelligence & image processing, Surgery, Computer Vision and Pattern Recognition, Affine transformation, Artificial intelligence, Tomography, X-Ray Computed, business, Algorithms
Abstract

International audience; Purpose: We propose to learn a 3D keypoint descriptor which we use to match keypoints extracted from full-body CT scans. Our methods are inspired by 2D keypoint descriptor learning, which was shown to outperform hand-crafted descriptors. Adapting these to 3D images is challenging because of the lack of labelled training data and high memory requirements.Method: We generate semi-synthetic training data. For that, we first estimate the distribution of local affine inter-subject transformations using labelled anatomical landmarks on a small subset of the database. We then sample a large number of transformations and warp unlabelled CT scans, for which we can subsequently establish reliable keypoint correspondences using guided matching. These correspondences serve as training data for our descriptor, which we represent by a CNN and train using the triplet loss with online triplet mining.Results: We carry out experiments on a synthetic data reliability benchmark and a registration task involving 20 CT volumes with anatomical landmarks used for evaluation purposes. Our learned descriptor outperforms the 3D-SURF descriptor on both benchmarks while having a similar runtime.Conclusion: We propose a new method to generate semi-synthetic data and a new learned 3D keypoint descriptor. Experiments show improvement compared to a hand-crafted descriptor. This is promising as literature has shown that jointly learning a detector and a descriptor gives further performance boost.

Published
2021

4. Regional myocardial function at preclinical disease stage of hypertrophic cardiomyopathy in female gene variant carriers

Author

Tjeerd Germans, Celine Seegers, Albert C. van Rossum, Pierre Croisille, Patrick Clarysse, Jolanda van der Velden, Rahana Y. Parbhudayal, Cardiology, ACS - Heart failure & arrhythmias, Physiology, Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE)

Subjects
Male, Cardiac function curve, medicine.medical_specialty, TNNT2, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Diastole, Magnetic Resonance Imaging, Cine, 030204 cardiovascular system & hematology, Asymptomatic, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, MYBPC3, MYH7, 0302 clinical medicine, Predictive Value of Tests, Internal medicine, medicine, Humans, Radiology, Nuclear Medicine and imaging, Cardiac imaging, Original Paper, medicine.diagnostic_test, business.industry, Myocardium, Hypertrophic cardiomyopathy, Heart, Tissue tagging, Magnetic resonance imaging, Cardiomyopathy, Hypertrophic, medicine.disease, Cardiology, Female, medicine.symptom, Cardiology and Cardiovascular Medicine, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

We recently showed more severe diastolic dysfunction at the time of myectomy in female compared to male patients with obstructive hypertrophic cardiomyopathy. Early recognition of aberrant cardiac contracility using cardiovascular magnetic resonance (CMR) imaging may identify women at risk of cardiac dysfunction. To define myocardial function at an early disease stage, we studied regional cardiac function using CMR imaging with tissue tagging in asymptomatic female gene variant carriers. CMR imaging with tissue tagging was done in 13 MYBPC3, 11 MYH7 and 6 TNNT2 gene carriers and 16 age-matched controls. Regional peak circumferential strain was derived from tissue tagging images of the basal and midventricular segments of the septum and lateral wall. Left ventricular wall thickness and global function were comparable between MYBPC3, MYH7, TNNT2 carriers and controls. MYH7 gene variant carriers showed a different strain pattern as compared to the other groups, with higher septal peak circumferential strain at the basal segments compared to the lateral wall, whereas MYBPC3, TNNT2 carriers and controls showed higher strain at the lateral wall compared to the septum. Only subtle gene-specific changes in strain pattern occur in the myocardium preceding development of cardiac hypertrophy. Overall, our study shows that there are no major contractile deficits in asymptomatic females carrying a pathogenic gene variant, which would justify the use of CMR imaging for earlier diagnosis.

Published
2021

5. Real Time Intraoperative Functional Brain Mapping Based on RGB Imaging

Author

C. Caredda, Michaël Sdika, Jacques Guyotat, Raphaël Sablong, Bruno Montcel, Laurent Mahieu-Williame, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Plateforme d'Imagerie Multimodale LyonTech (PILoT), Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Hospices Civils de Lyon (HCL), and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL)

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer science, [SDV]Life Sciences [q-bio], 0206 medical engineering, Biomedical Engineering, Biophysics, 02 engineering and technology, Human brain, 020601 biomedical engineering, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Functional brain, 0302 clinical medicine, Optical imaging, medicine.anatomical_structure, Robustness (computer science), Cortex (anatomy), medicine, White light, RGB color model, Neuroscience, ComputingMilieux_MISCELLANEOUS, Electrical brain stimulation
Abstract

Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. However, it still lacks robustness to be used as a clinical standard. In particular new biomarkers of brain functionality with improved sensitivity and specificity are needed. We present a method for the real time identification of the activated cortical areas based on the analysis of the cortical hemodynamic using a RGB camera and a white light source. We measure the quantitative oxy and deoxy-hemoglobin concentration changes in the human brain cortex with the modified Beer-Lambert law and Monte Carlo simulations. A functional model has been implemented to define in real time a binary biomarker of the cortical activation following neuronal activation by physiological stimuli. The results show a good correlation between the computed activation maps and the brain areas localized by electrical brain stimulation. We demonstrate that a RGB camera combined with a quantitative modeling of brain hemodynamics biomarkers can evaluate in real time the functional areas during neurosurgery and serve as a tool of choice to complement electrical brain stimulation.

Published
2021

6. Hydrophone Array Optimization, Conception, and Validation for Localization of Acoustic Sources in Deep-Sea Mining

Author

Arthur Finez, Simon Bouley, Florent Fayet, Barbara Nicolas, Jerome Mars, Valentin Baron, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), MicrodB, OSEAN S.A.S [Le Pradet], Osean, Nicolas, Barbara, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Beamforming, Ground truth, source localization, Hydrophone, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, 010505 oceanography, Computer science, Mechanical Engineering, Acoustics, 020206 networking & telecommunications, Ocean Engineering, 02 engineering and technology, 01 natural sciences, deep-sea mining, Deep sea mining, underwater acoustics, Noise, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, 14. Life underwater, Electrical and Electronic Engineering, Underwater, Underwater acoustics, Image resolution, 0105 earth and related environmental sciences
Abstract

International audience; As the mining of deep-sea natural deposits is becoming cost competitive compared to similar land-based mining, companies have started to dig into the seabeds to collect minerals. However, the acoustic contribution of this activity in the surrounding environment can be significant. To predict the impact of such noise, the starting point is to localize and quantify the sources that create it. In this study, a 3-D prototype acoustic array to perform this localization and quantification is designed, built, and deployed at sea for validation of its localization capacities. The design method performs a two-step study to define the array shape and select the hydrophone arrangement over it, under harsh constraints. Each step relies on two metrics to rank the candidates: the maximum sidelobe level, and the spatial resolution. These are computed on conventional beamforming maps for simulated sources that represent excavation machines on the ground. The shape is first determined to be the one that yields steady maximum sidelobe value levels over frequency. Second, the hydrophone arrangement that achieves the lowest maximum sidelobe level while limiting the spatial resolution is selected. This leads to a tip down conical array with 21 hydrophones, of about 3 m in height and diameter, and this is manufactured and used during an experimental campaign in the Mediterranean Sea. The experimental localization maps show strong agreement between the estimated source position and its ground truth. A more detailed comparison between simulated and real performances confirms accurate array conception and realization. Thus, this design procedure provides an efficient underwater acoustic array for monitoring deep-sea mining, the localization capacities of which are validated in a real-life setting.

Published
2021

7. Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach

Author

Abascal, Juan F P J, Abascal, Juan Fpj, Ducros, Nicolas, Pronina, Valeriya, Rit, Simon, Rodesch, Pierre-Antoine, Broussaud, Thomas, Bussod, Suzanne, Douek, Philippe, Hauptmann, Andreas, Arridge, Simon, PEYRIN, Françoise, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
General Computer Science, Computer science, Computed tomography, 02 engineering and technology, Iterative reconstruction, transfer learning, Regularization (mathematics), Convolutional neural network, 030218 nuclear medicine & medical imaging, Spectral CT, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 0202 electrical engineering, electronic engineering, information engineering, medicine, General Materials Science, Electrical and Electronic Engineering, medicine.diagnostic_test, business.industry, Deep learning, General Engineering, deep learning, Experimental data, Decomposition, Nonlinear system, inverse problem, 020201 artificial intelligence & image processing, lcsh:Electrical engineering. Electronics. Nuclear engineering, Artificial intelligence, business, lcsh:TK1-9971, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm
Abstract

International audience; The state-of-the art for solving the nonlinear material decomposition problem in spectral computed tomogra-phy is based on variational methods, but these are computationally slow and critically depend on the particular choice of the regularization functional. Convolutional neural networks have been proposed for addressing these issues. However, learning algorithms require large amounts of experimental data sets. We propose a deep learning strategy for solving the material decomposition problem based on a U-Net architecture and a Sim2Real transfer learning approach where the knowledge that we learn from synthetic data is transferred to a real-world scenario. In order for this approach to work, synthetic data must be realistic and representative of the experimental data. For this purpose, numerical phantoms are generated from human CT volumes of the KiTS19 Challenge dataset, segmented into specic materials (soft tissue and bone). These volumes are projected into sinogram space in order to simulate photon counting data, taking into account the energy response of the scanner. The network is trained to decompose the materials in the projection domain after which we apply any conventional tomographic method to reconstruct the dierent material volumes. The proposed decomposition method is compared to a regularized Gauss-Newton (RGN) method on synthetic data, experimental phantom data and human thorax data.

Published
2021

8. Evaluation of GATE‐RTion (GATE/Geant4) Monte Carlo simulation settings for proton pencil beam scanning quality assurance

Author

Susanna Guatelli, Michael Taylor, David Boersma, A. Aitkenhead, Carla Winterhalter, L. Grevillot, Peter Sitch, A. Resch, Marie Vidal, David Sarrut, Karen J. Kirkby, Vladimir Ivanchenko, Ranald I Mackay, Lydia Maigne, A. Elia, Division of Cancer Sciences, University of Manchester [Manchester], The Christie NHS Foundation Trust, Austrian Center for Mediacl Innovation and Technology [Wiener Neustadt] (ACMIT), Austrian Center for Mediacl Innovation and Technology, EBG MedAustron GmbH, Centre for Medical Radiation Physics, University of Wollongong [Australia], Laboratoire de Physique de Clermont (LPC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), European Organization for Nuclear Research (CERN), Tomsk State University [Tomsk], Medizinische Universität Wien = Medical University of Vienna, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre de Lutte contre le Cancer Antoine Lacassagne [Nice] (UNICANCER/CAL), UNICANCER-Université Côte d'Azur (UCA), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Monte Carlo method, Geant4, dose calculation, Imaging phantom, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Proton Therapy, Range (statistics), proton pencil beam scanning therapy, Humans, Computer Simulation, Radiometry, Pencil-beam scanning, Monte Carlo, Proton therapy, Physics, business.industry, Radiotherapy Planning, Computer-Assisted, Radiotherapy Dosage, General Medicine, GATE-RTion, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computational physics, 030220 oncology & carcinogenesis, Tomography, Protons, business, Monte Carlo Method, Quality assurance, Beam (structure)
Abstract

International audience; Purpose: Geant4 is a multi-purpose Monte Carlo simulation tool for modeling particle transport in matter. It provides a wide range of settings, which the user may optimize for their specific application. This study investigates GATE/Geant4 parameter settings for proton pencil beam scanning therapy.Methods: GATE8.1/Geant4.10.3.p03 (matching the versions used in GATE-RTion1.0) simulations were performed with a set of prebuilt Geant4 physics lists (QGSP_BIC, QGSP_BIC_EMY, QGSP_BIC_EMZ, QGSP_BIC_HP_EMZ), using 0.1mm-10mm as production cuts on secondary particles (electrons, photons, positrons) and varying the maximum step size of protons (0.1mm, 1mm, none). The results of the simulations were compared to measurement data taken during clinical patient specific quality assurance at The Christie NHS Foundation Trust pencil beam scanning proton therapy facility. Additionally, the influence of simulation settings was quantified in a realistic patient anatomy based on computer tomography (CT) scans.Results: When comparing the different physics lists, only the results (ranges in water) obtained with QGSP_BIC (G4EMStandardPhysics_Option0) depend on the maximum step size. There is clinically negligible difference in the target region when using High Precision neutron models (HP) for dose calculations. The EMZ electromagnetic constructor provides a closer agreement (within 0.35 mm) to measured beam sizes in air, but yields up to 20% longer execution times compared to the EMY electromagnetic constructor (maximum beam size difference 0.79 mm). The impact of this on patient-specific quality assurance simulations is clinically negligible, with a 97% average 2%/2 mm gamma pass rate for both physics lists. However, when considering the CT-based patient model, dose deviations up to 2.4% are observed. Production cuts do not substantially influence dosimetric results in solid water, but lead to dose differences of up to 4.1% in the patient CT. Small (compared to voxel size) production cuts increase execution times by factors of 5 (solid water) and 2 (patient CT).Conclusions: Taking both efficiency and dose accuracy into account and considering voxel sizes with 2 mm linear size, the authors recommend the following Geant4 settings to simulate patient specific quality assurance measurements: No step limiter on proton tracks; production cuts of 1 mm for electrons, photons and positrons (in the phantom and range-shifter) and 10 mm (world); best agreement to measurement data was found for QGSP_BIC_EMZ reference physics list at the cost of 20% increased execution times compared to QGSP_BIC_EMY. For simulations considering the patient CT model, the following settings are recommended: No step limiter on proton tracks; production cuts of 1 mm for electrons, photons and positrons (phantom/range-shifter) and 10 mm (world) if the goal is to achieve sufficient dosimetric accuracy to ensure that a plan is clinically safe; or 0.1 mm (phantom/range-shifter) and 1 mm (world) if higher dosimetric accuracy is needed (increasing execution times by a factor of 2); most accurate results expected for QGSP_BIC_EMZ reference physics list, at the cost of 10-20% increased execution times compared to QGSP_BIC_EMY.

Published
2020

9. Carotid Wall Longitudinal Motion in Ultrasound Imaging: An Expert Consensus Review

Author

Didier Vray, Rytis Jurkonis, Aimilia Gastounioti, Zhifan Gao, André Sérusclat, Konstantina S. Nikita, Spyretta Golemati, Guillaume Zahnd, Tomi Laitinen, Fereshteh Yousefi Rizi, Maciej Orkisz, Heikki Yli-Ollila, Jason S. Au, Maureen J. MacDonald, Monika Makūnaitė, Hamid Behnam, Tiina M. Laitinen, Nassir Navab, University of Teheran, University of Waterloo [Waterloo], Kanta-Häme Central Hospital, Kuopio University Hospital, National and Kapodistrian University of Athens (NKUA), Kaunas University of Technology (KTU), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Computer Aided Medical Procedures & Augmented Reality (CAMPAR), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), McMaster University [Hamilton, Ontario], Iran University of Science and Technology [Tehran] (IUST), National Sun Yat-Sen University (NSYSU), Perelman School of Medicine, University of Pennsylvania [Philadelphia], Imagerie Ultrasonore, Hôpital Louis Pradel [CHU - HCL], Hospices Civils de Lyon (HCL), National Technical University of Athens [Athens] (NTUA), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
medicine.medical_specialty, Consensus, Acoustics and Ultrasonics, Arteriosclerosis, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Carotid arteries, Biophysics, 030204 cardiovascular system & hematology, Longitudinal motion, Motion estimation, Motion (physics), 030218 nuclear medicine & medical imaging, Vascular health, Motion, 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, Ultrasound, medicine, Humans, Radiology, Nuclear Medicine and imaging, In patient, Wall motion, Cardiovascular risk factors, Ultrasonography, Speckle tracking, Radiological and Ultrasound Technology, business.industry, Block matching, Expert consensus, Atherosclerosis, 3. Good health, Carotid Arteries, Practice Guidelines as Topic, Ultrasound imaging, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Carotid artery, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

International audience; Motion extracted from the carotid artery wall provides unique information for vascular health evaluation. Carotid artery longitudinal wall motion corresponds to the multiphasic arterial wall excursion in the direction parallel to blood flow during the cardiac cycle. While this motion phenomenon has been well characterized, there is a general lack of awareness regarding its implications for vascular health assessment or even basic vascular physiology. In the last decade, novel estimation strategies and clinical investigations have greatly advanced our understanding of the bi-axial behavior of the carotid artery, necessitating an up-to-date review to summarize and classify the published literature in collaboration with technical and clinical experts in the field. Within this review, the state-of-the-art methodologies for carotid wall motion estimation are described, and the observed relationships between longitudinal motion-derived indices and vascular health are reported. The vast number of studies describing the longitudinal motion pattern in plaque-free arteries, with its putative application to cardiovascular disease prediction, point to the need for characterizing the added value and applicability of longitudinal motion beyond established biomarkers. To this aim, the main purpose of this review was to provide a strong base of theoretical knowledge, together with a curated set of practical guidelines and recommendations for longitudinal motion estimation in patients, to foster future discoveries in the field, toward the integration of longitudinal motion in basic science as well as clinical practice.

Published
2020

10. Serial and Parallel Active Decoupling Characterization Using RF MEMS Switches for Receiver Endoluminal Coils at 1.5 T

Author

Isabelle Saniour, Fraser Robb, Olivier Beuf, Simon A. Lambert, Kevin Tse Ve Koon, Hamza Raki, Henri Souchay, Beuf, Olivier, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), General Electric Medical Systems [Buc] (GE Healthcare), General Electric Medical Systems, GE Healthcare, MRI coils division, GE Healthcare (GEHC), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Scanner, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Active decoupling, MR switch, Topology, 01 natural sciences, Optical switch, endoluminal coils, law.invention, law, Electrical and Electronic Engineering, Instrumentation, ComputingMilieux_MISCELLANEOUS, Microelectromechanical systems, Physics, 010401 analytical chemistry, Colon wall, PIN diode, [SPI.TRON] Engineering Sciences [physics]/Electronics, [SPI.TRON]Engineering Sciences [physics]/Electronics, 0104 chemical sciences, Mechanical system, MEMS, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Radio frequency, Decoupling (electronics)
Abstract

MEMS (Micro Electro Mechanical System) switches were assessed and compared to PIN diode in fulfilling the task of active decoupling of Receiver Endoluminal Coils (RECs). Three prototype RECs with the PIN diode in parallel ( ${p}$ PIN), MEMS in parallel ( ${p}$ MEMS) and MEMS in series ( ${s}$ MEMS) with the REC loop were built. Quality factors (Q-values), decoupling efficiency and switching delays were characterized on bench and Signal-to-Noise Ratios (SNRs) established on images at 1.5 T. Q-values were equal to 62.5, 41.2 and 65.1 for ${p}$ PIN, ${s}$ MEMS and ${p}$ MEMS, respectively. In the decoupled state, reflection coefficients S11 and S21 at resonance frequency both indicated proper decoupling. Switching delays were less than $0.7~\mu \text{s}$ and $10~\mu \text{s}$ for ${p}$ PIN and MEMS RECs, respectively. Decoupling/coupling delays of MEMS remained compatible with most Magnetic Resonance (MR) clinical applications. For all prototypes, MR images displayed no signal saturation and similar elliptical image sensitivity patterns. No artifacts due to active decoupling failure were observed. Mean SNR values obtained with ${p}$ MEMS REC were higher than those obtained with ${s}$ MEMS REC but lower than with ${p}$ PIN REC because of the use of additional instrumentation to render the scanner compatible with the MEMS utilization. MEMS in parallel are an interesting alternative to PIN diode for decoupling and could lead to better SNR with a compatible MR system (dedicated control signal). The MEMS in series can be used for both decoupling and reconfiguration of the REC loop geometry for colon wall examination.

Published
2020

11. Matrix Metalloproteinase-9 and Monocyte Chemoattractant Protein-1 Are Associated With Collateral Status in Acute Ischemic Stroke With Large Vessel Occlusion

Author

Camille Amaz, Nathan Mewton, Yves Berthezène, Claire Crola Da Silva, Marielle Buisson, Thomas Bochaton, Nathalie Dufay, Elodie Ong, Omer Eker, Alexandre Paccalet, Norbert Nighoghossian, Laurent Derex, Michel Ovize, Tae-Hee Cho, Laura Mechtouff, Stroke Department, Neurological Hospital, Hospices Civils de Lyon (HCL), Cardiovasculaire, métabolisme, diabétologie et nutrition (CarMeN), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Hospices Civils de Lyon (HCL), Virologie et pathogenèse virale (VPV), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche et d'Application en Traitement de l'Image et du Signal (CREATIS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École Supérieure Chimie Physique Électronique de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Neuroradiology Department, Neurological Hospital, Department of Cardiology, and Imagerie Tomographique et Radiothérapie

Subjects
Monocyte Chemoattractant Proteins, medicine.medical_specialty, [SDV]Life Sciences [q-bio], 030204 cardiovascular system & hematology, digital subtraction angiography, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, medicine, collateral circulation, Acute ischemic stroke, brain edema, Advanced and Specialized Nursing, medicine.diagnostic_test, business.industry, Matrix metalloproteinase 9, Digital subtraction angiography, Collateral circulation, Inflammatory biomarkers, Cardiology, matrix metalloproteinase 9, Neurology (clinical), monocyte chemoattractant proteins, Cardiology and Cardiovascular Medicine, business, 030217 neurology & neurosurgery, Large vessel occlusion, Monocyte chemoattractant protein
Abstract

Background and Purpose: In ischemic stroke, inflammatory status may condition the development of collateral circulation. Here we assessed the relationship between systemic inflammatory biomarkers and collateral status in large vessel occlusion before mechanical thrombectomy. Methods: HIBISCUS-STROKE is a cohort study including acute ischemic stroke patients with large vessel occlusion treated with mechanical thrombectomy following admission magnetic resonance imaging. MMP-9 (matrix metalloproteinase-9) and MCP-1 (monocyte chemoattractant protein-1) were measured on blood sampling collected at admission. Collateral status was assessed on pretreatment Digital subtraction angiography and categorized into poor (Higashida score, 0–2) and good (Higashida score, 3–4). A multiple logistic regression model was performed to detect independent markers of good collateral status. Results: One hundred and twenty-two patients were included, of them 71 patients (58.2%) had a good collateral status. In univariate analysis, low MMP-9 levels ( P =0.01), high MCP-1 levels ( P P =0.046), a high diastolic blood pressure ( P =0.049), the absence of tandem occlusion ( P =0.046), a high Alberta Stroke Program Early CT Score ( P P P =0.02) and high MCP-1 levels ( P Conclusions: Low MMP-9 and high MCP-1 levels were associated with good pretreatment collateral status in patients with acute ischemic stroke with large vessel occlusion. These results might suggest a relationship between collateral status and inflammation.

Published
2020

12. A non-human primate model of stroke reproducing endovascular thrombectomy and allowing long-term imaging and neurological read-outs

Author

F. Taborik, Karine Portier, Norbert Nighoghossian, Adrien Oudotte, Michaël Verset, Thomas Troalen, Omer Eker, Denis Vivien, Inés Mérida, Christelle Leon, Christian Tourvieille, Didier Le Bars, Emmanuelle Canet-Soulas, Michel Ovize, Hugues Contamin, Nicolas Costes, Nikolaos Makris, Véronique Agin, Joachim Confais, Tae-Hee Cho, Mohamed Aggour, Sophie Lancelot, Justine Debatisse, Jean-Baptiste Langlois, Océane Wateau, Marjorie Villien, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Recherche et d'Application en Traitement de l'Image et du Signal (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École Supérieure Chimie Physique Électronique de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre d'Etude et de Recherche Multimodal Et Pluridisciplinaire en imagerie du vivant (CERMEP - imagerie du vivant), Université de Lyon-Université de Lyon-CHU Grenoble-Hospices Civils de Lyon (HCL)-CHU Saint-Etienne-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Laboratoire d'Ecologie des Hydrosystèmes Naturels et Anthropisés (LEHNA), Université de Lyon-Université de Lyon-École Nationale des Travaux Publics de l'État (ENTPE)-Centre National de la Recherche Scientifique (CNRS), Cynbiose, CYNBIOSE, Institut Claude Bourgelat (ICLB), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Cardiovasculaire, métabolisme, diabétologie et nutrition (CarMeN), Université de Lyon-Université de Lyon-Hospices Civils de Lyon (HCL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Siemens Healthcare [France], Siemens AG [Munich], Institute of Electronic Structure and Laser (FORTH-IESL), Foundation for Research and Technology - Hellas (FORTH), Radiopharmaceutical and Neurochemical Biomarkers Team (BioRaN), Centre de recherche en neurosciences de Lyon (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Department of Cardiology, Hospices Civils de Lyon (HCL), Laboratoire de Chimie de la Matière Condensée de Paris (site ENSCP) (LCMCP (site ENSCP)), Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Collège de France (CdF (institution))-Institut de Chimie du CNRS (INC), French Ministry of Higher Education and Research (ANRT), ANR-15-CE17-0020,CYCLOPS,CYCLOsporine A : effet neuroprotecteur dans un modèle Primate de Stroke en imagerie(2015), ANR-16-RHUS-0009,MARVELOUS,MARVELOUS(2016), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Université de Lyon-École Supérieure de Chimie Physique Électronique de Lyon (CPE)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université Pierre et Marie Curie - Paris 6 (UPMC)-Collège de France (CdF (institution))-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, [SDV]Life Sciences [q-bio], Executive Function, 0302 clinical medicine, Occlusion, Stroke, Thrombectomy, 0303 health sciences, Behavior, Animal, Endovascular Procedures, Infarction, Middle Cerebral Artery, Magnetic Resonance Imaging, 3. Good health, Treatment Outcome, Neurology, Blood-Brain Barrier, Motor Skills, Reperfusion Injury, Middle cerebral artery, Cardiology, medicine.symptom, Cardiology and Cardiovascular Medicine, medicine.medical_specialty, Ischemia, Context (language use), ischemia–reperfusion, Asymptomatic, Lesion, 03 medical and health sciences, medicine.artery, Internal medicine, medicine, Animals, cardiovascular diseases, Thrombus, Ischemic Stroke, 030304 developmental biology, business.industry, Original Articles, medicine.disease, Disease Models, Animal, Macaca fascicularis, Endovascular non-human primate stroke model, PET-MRI imaging, Positron-Emission Tomography, neurofunctional tests, Neurology (clinical), Tomography, X-Ray Computed, business, 030217 neurology & neurosurgery
Abstract

International audience; Stroke is a devastating disease. Endovascular mechanical thrombectomy is dramatically changing the management of acute ischemic stroke, raising new challenges regarding brain outcome and opening up new avenues for brain protection. In this context, relevant experiment models are required for testing new therapies and addressing important questions about infarct progression despite successful recanalization, reversibility of ischemic lesions, blood-brain barrier disruption and reperfusion damage. Here, we developed a minimally invasive non-human primate model of cerebral ischemia (Macaca fascicularis) based on an endovascular transient occlusion and recanalization of the middle cerebral artery (MCA). We evaluated per-occlusion and post-recanalization impairment on PET-MRI, in addition to acute and chronic neuro-functional assessment. Voxel-based analyses between per-occlusion PET-MRI and day-7 MRI showed two different patterns of lesion evolution: "symptomatic salvaged tissue" (SST) and "asymptomatic infarcted tissue" (AIT). Extended SST was present in all cases. AIT, remote from the area at risk, represented 45% of the final lesion. This model also expresses both worsening of fine motor skills and dysexecutive behavior over the chronic post-stroke period, a result in agreement with cortical-subcortical lesions. We thus fully characterized an original translational model of ischemia-reperfusion damage after stroke, with consistent ischemia time, and thrombus retrieval for effective recanalization.

Published
2020

13. National dose reference levels in computed tomography–guided interventional procedures—a proposal

Author

Patrick Baur, Olivier Rouvière, Arthur David, Robert Kovacs, Marie Eresue-Bony, Alexandre Malakhia, Romaric Dal, Olivier Andreani, Audrey Fohlen, Eric de Kerviler, Marie Faruch, Mathilde Demonchy, Eric Decoux, Laurie Cabrol-Faivre, Julien Le Roy, Lambros Tselikas, Célian Michel, Lucie Cassagnes, Frederic Lafay, Anthony Dohan, Claire Boutet, Emeline Bigand, Aurélie Vuillod, Christophe Teriitehau, Lama Hadid-Beurrier, Romain Gillet, Thierry Arnaud, Djamel Dabli, Yves Barbotteau, Rabih Alwan, J. Frandon, Bernard Woerly, Nathalie Bestion, Emilie Alonso, Xavier Stefanovic, Valérie Bousson, Simon Henry, Pierre Yves Brillet, Boris Guiu, Cynthia Majorel-Gouthain, Franck Couzon, Alexis Jacquier, Joël Greffier, Mélody Potel, F. Pilleul, Thibaut Jacques, Loic Boussel, Didier Defez, Aymeric Rauch, Nicolas Sans, Eric Pessis, Aurélie Moussier-Lherm, Marc Andre, Jean Paul Beregi, Olivier Ernst, Frederic Douane, Laurent Hennequin, Thomas Hebert, Romain Gautier, Gilbert Ferretti, Sylvie Monfraix, Bouchra Habib-Geryes, Julia Rousseau, Sébastien Aubry, David Boutteau, Marc Haberlay, Florian Magnier, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Adult, Male, medicine.medical_specialty, Percutaneous, Adolescent, Biopsy, Computed tomography, Radiation Dosage, Radiography, Interventional, 030218 nuclear medicine & medical imaging, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Reference Values, Surveys and Questionnaires, medicine, Humans, Radiology, Nuclear Medicine and imaging, In patient, ComputingMilieux_MISCELLANEOUS, Aged, Retrospective Studies, Neuroradiology, Aged, 80 and over, Vertebroplasty, medicine.diagnostic_test, business.industry, Reproducibility of Results, Interventional radiology, General Medicine, Middle Aged, Spine, 3. Good health, Quartile, Fluoroscopy, 030220 oncology & carcinogenesis, Total dose, Female, France, Radiology, Tomography, X-Ray Computed, business, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

To establish national reference levels (RLs) in interventional procedures under CT guidance as required by the 2013/59/Euratom European Directive. Seventeen categories of interventional procedures in thoracic, abdominopelvic, and osteoarticular specialties (percutaneous infiltration, vertebroplasty, biopsy, drainage, tumor destruction) were analyzed. Total dose length product (DLP), number of helical acquisitions (NH), and total DLP for helical, sequential, or fluoroscopic acquisitions were recorded for 10 to 20 patients per procedure at each center. RLs were calculated as the 3rd quartiles of the distributions and target values for optimization process (TVOs) as the median. RLs and TVOs were compared with previously published studies. Results on 5001 procedures from 49 centers confirmed the great variability in patient dose for the same category of procedures. RLs were proposed for the DLPs and NHs in the seventeen categories. RLs in terms of DLP and NH were 375 mGy.cm and 2 NH for spinal or peri-spinal infiltration, 1630 mGy.cm and 3 NH for vertebroplasty, 845 mGy.cm and 4 NH for biopsy, 1950 mGy.cm and 8 NH for destruction of tumors, and 1090 mGy.cm and 5 NH for drainage. DLP and NH increased with the complexity of procedures. This study was the first nationwide multicentric survey to propose RLs for interventional procedures under CT guidance. Heterogeneity of practice in centers were found with different levels of patient doses for the same procedure. The proposed RLs will allow imaging departments to benchmark their practice with others and optimize their protocols. • National reference levels are proposed for 17 categories of interventional procedures under CT guidance. • Reference levels are useful for benchmarking practices and optimizing protocols. • Reference levels are proposed for dose length product and the number of helical acquisitions.

Published
2020

14. Une forme de référence mutuelle pour la fusion et l'évaluation des méthodes de segmentation

Author

Jehan-Besson, S., Clouard, R., Tilmant, C., de Cesare, A., Lalande, A., Lebenberg, J., Clarysse, P., Sarry, L., Frouin, F., Garreau, M., Laboratoire d'Imagerie Translationnelle en Oncologie (LITO ), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Curie [Paris], Equipe Image - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS), Institut Pascal (IP), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Equipe IFTIM [ImViA - EA7535], Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon)-Centre Régional de Lutte contre le cancer Georges-François Leclerc [Dijon] (UNICANCER/CRLCC-CGFL), UNICANCER-UNICANCER-Imagerie et Vision Artificielle [Dijon] (ImViA), Université de Bourgogne (UB)-Université de Bourgogne (UB), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Image Science for Interventional Techniques (ISIT), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Centre National de la Recherche Scientifique (CNRS), Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-SIGMA Clermont (SIGMA Clermont)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Imagerie Biomédicale (LIB), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université d'Auvergne - Clermont-Ferrand I (UdA)-Centre National de la Recherche Scientifique (CNRS)-Clermont Université, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)

Subjects
active contours, variational approaches, segmentation evaluation, shape gradients, [INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT], Computer Science::Computer Vision and Pattern Recognition, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Mutual shape, segmentation, shape optimization, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, segmentation fusion
Abstract

This paper proposes the estimation of a mutual shape from a set of different segmentation results using both active contours and information theory. The mutual shape is here defined as a consensus shape estimated from a set of different segmentations of the same object. In an original manner, such a shape is defined as the minimum of a criterion that benefits from both the mutual information and the joint entropy of the input segmentations. This energy criterion is justified using similarities between information theory quantities and area measures, and presented in a continuous variational framework. In order to solve this shape optimization problem, shape derivatives are computed for each term of the criterion and interpreted as an evolution equation of an active contour. A mutual shape is then estimated together with the sensitivity and specificity of each segmentation. Some synthetic examples allow us to cast the light on the difference between the mutual shape and an average shape. The applicability of our framework has also been tested for segmentation evaluation and fusion of different types of real images (natural color images, old manuscripts, medical images).

Published
2022

15. Use of fast realistic simulations on GPU to extract CAD models from microtomographic data in the presence of strong CT artefacts

Author

Franck Vidal, Jean Michel Létang, Iwan T. Mitchell, Bangor University, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Computer science, CAD, Numerical simulation, Evolutionary computation, 01 natural sciences, 030218 nuclear medicine & medical imaging, 010309 optics, 03 medical and health sciences, 0302 clinical medicine, High fidelity, 0103 physical sciences, X-rays, Computer vision, Optimisation, Computed tomography, Impulse response, Computer simulation, Computer aided analysis, business.industry, Detector, General Engineering, Process (computing), Image segmentation, [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Artificial intelligence, High performance computing, business
Abstract

International audience; The presence of strong imaging artefacts in microtomographic X-ray data makes the CAD modelling process difficult to carry out. As an alternative to traditional image segmentation techniques, we propose to register the CAD models by deploying a realistic X-ray simulation on GPU in an optimisation framework. A user study was also conducted to compare the measurements made manually by a cohort of volunteers and those produced with our framework. Our implementation relies on open source software only. We numerically modelled the real experiment, taking into account geometrical properties as well as beam hardening, impulse response of the detector, phase contrast, and photon noise. Parameters of the overall model are then optimised so that X-ray projections of the registered the CAD models match the projections from an actual experiment. It appeared that manual measurements can be variable and subject to bias whereas our framework produced more reliable results. The features seen in the real CT image, including artefacts, were accurately replicated in the CT image reconstructed from the simulated data after registration: (i) linear attenuation coefficients are comparable for all the materials, (ii) geometrical properties are accurately recovered, and (iii) simulated images reproduce observed experimental artefacts. We showed that the choice of objective function is crucial to produce high fidelity results. We also demonstrated how to automatically produce CAD models as an optimisation problem, producing a high cross-correlation between the experimental CT slice and the simulated CT slice. These results pave the way towards the use of fast realistic simulation for accurate CAD modelling in tomographic X-ray data

Published
2022

16. 3D denoised completion network for deep single-pixel reconstruction of hyperspectral images

Author

Nicolas Ducros, Françoise Peyrin, Juan F. P. J. Abascal, Valeriya Pronina, Dmitry V. Dylov, Antonio Lorente Mur, Skolkovo Institute of Science and Technology [Moscow] (Skoltech), Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), ANR-17-CE19-0003,Armoni,Acquisition Rapide d'images biomédicales par caméra MONo-pIxel(2017), Ducros, Nicolas, and Acquisition Rapide d'images biomédicales par caméra MONo-pIxel - - Armoni2017 - ANR-17-CE19-0003 - AAPG2017 - VALID

Subjects
[SPI.OPTI] Engineering Sciences [physics]/Optics / Photonic, Underdetermined system, Computer science, Multispectral image, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Iterative reconstruction, 01 natural sciences, Grayscale, Convolution, 010309 optics, Tikhonov regularization, Optics, 0103 physical sciences, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, ComputingMilieux_MISCELLANEOUS, business.industry, Hyperspectral imaging, Pattern recognition, Inverse problem, 021001 nanoscience & nanotechnology, Atomic and Molecular Physics, and Optics, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Computer Science::Computer Vision and Pattern Recognition, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, Artificial intelligence, 0210 nano-technology, business
Abstract

Single-pixel imaging acquires an image by measuring its coefficients in a transform domain, thanks to a spatial light modulator. However, as measurements are sequential, only a few coefficients can be measured in the real-time applications. Therefore, single-pixel reconstruction is usually an underdetermined inverse problem that requires regularization to obtain an appropriate solution. Combined with a spectral detector, the concept of single-pixel imaging allows for hyperspectral imaging. While each channel can be reconstructed independently, we propose to exploit the spectral redundancy between channels to regularize the reconstruction problem. In particular, we introduce a denoised completion network that includes 3D convolution filters. Contrary to black-box approaches, our network combines the classical Tikhonov theory with the deep learning methodology, leading to an explainable network. Considering both simulated and experimental data, we demonstrate that the proposed approach yields hyperspectral images with higher quantitative metrics than the approaches developed for grayscale images.

Published
2021

17. Apprentissage statistique des interactions entre forme et déformation cardiaques

Author

Di Folco, Maxime, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon - INSA Lyon, Patrick Clarysse(patrick.clarysse@creatis.insa-lyon.fr), and Nicolas Duchateau

Subjects
Manifold learning, Apprentissage de variétés, Myocardial deformation, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Imagerie cardiaque, Forme cardiaque, Déformation myocardique, Cardiac imaging, Dimensionality reduction, Shape analysis, Réduction de dimensions
Abstract

In clinical routine, medical imaging allows to extract descriptors or scalars characterizing the cardiac function and to establish a diagnosis. In heart failure, cardiac remodeling often occurs and several aspects of morphology and function are affected during this phenomenon, which can include shape and deformation abnormalities. In addition, interactions between these two aspects have been demonstrated structurally or related to certain cardiac pathologies. These interactions are difficult to analyze by simple scalar indices that generally describe a global behavior. Medical imaging is able to provide high-dimensional representations of these descriptors, i.e., regional/local information at several instants of the cardiac cycle. However, they are not exploited in clinical routine because of the lack of time, the lack of consensus, and the difficulty of their analysis. In this manuscript, we explore approaches to further characterize the partially-known link between cardiac shape and deformation via high-dimensional representations of both aspects. Computational anatomy or manifold learning methods allow to exploit these individual high-dimensional representations and generalize the analysis to a population. However, these methods generally consider only one aspect of cardiac function at a time, whereas several of them can interact. Methods incorporating multiple descriptors usually do not explicitly consider the possible link between them.This work has three main contributions. First, we propose a strategy to characterize the interactions between cardiac shape and deformation assessed by high-dimensional descriptors and demonstrate its relevance for several right ventricular pathologies. This strategy is based on a non-linear learning method (Multiple Manifold Alignment) and is used here to characterize a partially-known link, whereas it has been applied so far to descriptors belonging to the same manifold, and for which the link naturally exists. Secondly, we evaluated the benefit of taking into account the link between descriptors by studying the differences with an approach that considers each descriptor individually and other approaches that consider several descriptors jointly. Finally, the study of the influence of shape/deformation descriptors and normalization strategies on our approach has highlighted a possible bias introduced by the choices made and shown that the appropriate choice depends on the targeted application.This thesis shows the relevance of using manifold alignment to consider the partially-known link between shape and cardiac deformation by illustrating it in the comparative study of several right ventricular diseases. These analyses open the door to the exploitation of these coherent latent spaces for more applicative challenges such as risk quantification.; En routine clinique, les méthodes d'imagerie permettent d'extraire des descripteurs ou indices caractérisant la fonction cardiaque et d'établir un diagnostic. Dans le cas de l'insuffisance cardiaque, un remodelage cardiaque se produit très souvent. Plusieurs aspects de la morphologie et de la fonction sont affectés lors de ce phénomène, notamment des anomalies de forme et de déformation peuvent apparaître. De plus, des interactions entre ces deux aspects ont été mises en évidence structurellement ou reliées à certaines pathologies cardiaques. Ces interactions peuvent être difficilement analysées par de simples indices scalaires qui décrivent généralement un comportement global. L'imagerie médicale est en mesure de fournir des représentations de hautes dimensions de ces descripteurs, c'est-à-dire une information régionale/locale et à plusieurs instants du cycle cardiaque ; elles sont cependant non exploitées en routine clinique par manque de temps, de consensus et de la difficulté de leur analyse. Dans ce manuscrit, nous explorons des approches de caractérisation plus fine du lien partiellement connu entre la forme et la déformation cardiaques via des représentations hautes dimensions de ces deux aspects. Des méthodes d'anatomie computationnelle ou d'apprentissage de variétés permettent d'exploiter ces représentations hautes dimensions individuelles et de généraliser l'analyse à une population. Néanmoins, ces méthodes ne considèrent généralement qu'un aspect de la fonction cardiaque à la fois alors que plusieurs interagissent. Les méthodes intégrant plusieurs descripteurs ne prennent habituellement pas explicitement en compte le lien possible entre eux.Ce travail comporte trois principales contributions. Premièrement, nous proposons une stratégie pour caractériser les interactions entre la forme et la déformation cardiaques évaluées par des descripteurs hautes dimensions et démontrons sa pertinence pour plusieurs pathologies du ventricule droit. Cette stratégie est basée sur une méthode d'apprentissage non-linéaire (alignement de variétés multiples) et utilisée ici pour caractériser un lien partiellement connu alors qu'elle a été jusqu'à présent appliquée à des descripteurs évoluant dans la même variété, et pour lesquels le lien existe naturellement. Deuxièmement, nous avons évalué le bénéfice de prendre en compte le lien entre les descripteurs en étudiant les différences avec une approche qui considère chaque descripteur individuellement et d'autres approches prenant en compte plusieurs descripteurs. Pour finir, l'étude de l'influence des descripteurs de forme/déformation et de stratégies de normalisation sur notre approche a mis en valeur un possible biais introduit par les choix faits et montré que le choix approprié dépend de l'application visée.Cette thèse montre la pertinence d'utiliser l'alignement de variétés pour considérer le lien partiellement connu entre forme et déformation cardiaques en l'illustrant dans l'étude comparée de plusieurs pathologies du ventricule droit. Ces analyses ouvrent la porte à l'exploitation de ces espaces cohérents pour des challenges plus applicatifs comme la quantification de risques.

Published
2021

18. Spurious phase correction in rapid metabolic imaging

Author

Carine Chassain, Hélène Ratiney, Nour El Sabbagh, Guilhem Pages, Jean-Marie Bonny, Qualité des Produits Animaux (QuaPA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), AgroResonance, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Clermont Auvergne (UCA), RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), CHAZEIX, Corinne, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Nuclear and High Energy Physics, Scanner, [SDV]Life Sciences [q-bio], Physics::Medical Physics, Biophysics, Phase (waves), Biochemistry, Signal, Imaging phantom, Phase correction, 030218 nuclear medicine & medical imaging, Phase-coherent, 03 medical and health sciences, 0302 clinical medicine, Sampling (signal processing), Frequency commutation, Commutation, Time domain, Phase-continuous, Physics, Magnetic resonance spectroscopic imaging, Condensed Matter Physics, [SDV] Life Sciences [q-bio], Multi-shot imaging, MRSI, Algorithm, 030217 neurology & neurosurgery
Abstract

International audience; IDEAL-type magnetic resonance spectroscopic imaging (MRSI) sequences require the acquisition of several datasets using optimized sampling in the time domain to reconstruct metabolite maps. Each unitary scan consists of a selective slice (2D) or slab (3D) excitation followed by an evolution time and then the acquisition of the spatially encoded signal. It is critical that the phase variation during the evolution time for each scan is only dependent on chemical shifts. In this paper, we described the apparition of spurious phase due to either the transmit or the receive frequency. The presence of this unwanted phase depends on (i) where the commutation between these two frequencies is performed and (ii) how it is done, as there are two phase commutation modes: continuous and coherent. We present the correction needed in function of the different cases. It appears that some solutions are universal. However, it is critical to know which case is implemented on the MRI scanner, which is not always easy information to have. We illustrated several cases with our preclinical MRI by using the IDEAL spiral method on a 13C phantom. © 2021 Elsevier Inc.

Published
2021

19. Anonymizing motion sensor data through time-frequency domain

Author

Alain Dieterlen, Pierre Rouge, Carole Frindel, Ali Moukadem, Antoine Boutet, Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche en Informatique Mathématiques Automatique Signal - IRIMAS - UR 7499 (IRIMAS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Privacy Models, Architectures and Tools for the Information Society (PRIVATICS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Inria Lyon, Institut National de Recherche en Informatique et en Automatique (Inria), CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and University ofHaute Alsace

Subjects
Random Forest, Computer science, Feature extraction, Convolutional Neural Networks, Time-Frequency, Motion detection, computer.software_genre, Convolutional neural network, Time–frequency analysis, Activity, Activity recognition, Information sensitivity, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Privacy, Identity (object-oriented programming), Data mining, Representation (mathematics), computer
Abstract

International audience; The recent development of Internet of Things (IoT) has democratized activity monitoring. Even if the data collected can be useful for healthcare, sharing this sensitive information exposes users to privacy threats and re-identification. This paper presents two approaches to anonymize the motion sensor data. The first is an extension of an earlier work based on filtering in the time-frequency plane and convolutional neural network; and the second is based on handcrafted features extracted from the zeros distribution of the time-frequency representation. The two approaches are evaluated on a public dataset to assess the accuracy of activity recognition and user re-identification. With the first approach we obtained an accuracy rate in activity recognition of 73% while limiting the identity recognition to an accuracy rate of 30% which corresponds to an activity identity ratio of 2.4. With the second approach we succeeded in improving the activity and identity ratio to 2.67 by attaining an accuracy rate in activity recognition of 80% while maintaining the re-identification rate at 30%.

Published
2021

20. Dosimetric comparison between planning SPECT and monitoring PET for 90Y-radioembolisation

Author

Labour, Joey, Baudier, Thomas, Martino, Sandrine Parisse-Di, Khayi, F, Sarrut, David, Badel, J, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Centre Léon Bérard [Lyon]

Subjects
[PHYS.PHYS.PHYS-MED-PH]Physics [physics]/Physics [physics]/Medical Physics [physics.med-ph]
Abstract

International audience; Aim/Introduction:This study analysed different workflows to compare dosimetry computed from SPECT99m Tc-MAA images and PET treatment monitoring images following 90Y-radioembolisation.Materials and Methods:A cohort of 20 patients iscurrently being studied. For each patient, 99m-Tc-MAA SPECT/CT images were acquired, reconstructed with attenuation- and diffusion-corrections, and calibrated in MBq.mL-1. Treatment monitoring was performed with a digital PET/CT using reconstruction parameters [1] optimised with dose-volume histograms (DVHs). The whole liver (WL) and total tumour (T total) were contoured on the CT acquired during the workup session and propagated to the SPECT images. The perfused liver volume (PLV) was defined using a 5% threshold on the SPECT. Contours were propagated to PET images with four registration methods, rigid global, deformable global, rigid local and rigid local,functional. The two firsts consider CT (SPECT) to CT (PET) rigid/deformable (BSpline) transformations. The third uses a rigid transformation between the two CTs, centred on the live ronly. The last one considers rigid local registration between SPECT and PET images with a rigid local transformation centred on the liver. Absorbed dose distributions were calculated using Voxel S-values convolution. The predictive and treatment follow-up DVHs were compared using the mean absorbed dose (D mean) and absorbed dose at 2% volume (D 2%).Results:For the first patients, D mean were within 5.4% and 6.4% and D2% were within 2.0% and 2.1% for the WL and PLV whatever the registration method, respectively. However, for Ttotal, the range was larger. For example, for patient #3, the SPECT-predicted Dmean was125.4 Gy while it was 39.07, 43.6, 30.7 and 168.3 Gy from the PET images for all methods, respectively. In that case, only locally rigid registration between functional images (SPECT and PET) instead of CT images provided comparable DVHs. This might be due to inaccurate location of the lesions due to respiratory motion and internal organ movements between work up and treatment sessions. Even if liver CT contours were well registered, the activity measured from SPECT and PET, integrated over a longer acquisition duration than CT (12.5 min/bed for SPECT, 10 min/bed for PET) mismatchemismatched.Conclusion:rigid local,functional method is recommended in addition to other CT registration methods to propagate contours from SPECT/CT to PET/CT. The analysis on the entire cohort will be presented at the conference.References:[1] Labour, J. et al. "Yttrium-90Quantitative Phantom Study Using Digital Photon Counting PET." EJNMMI Physics, in revision, (2021).

Published
2021

21. Patient-specific dosimetric workflow with a reduced number of time-points for 177Lu treatments

Author

Vergnaud, Laure, Giraudet, A., Moreau, A., Baudier, Thomas, Badel, J.N., Sarrut, David, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Léon Bérard [Lyon], and Rayet, Béatrice

Subjects
[SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, [PHYS.PHYS.PHYS-MED-PH] Physics [physics]/Physics [physics]/Medical Physics [physics.med-ph], [SDV.CAN] Life Sciences [q-bio]/Cancer, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [PHYS.PHYS.PHYS-MED-PH]Physics [physics]/Physics [physics]/Medical Physics [physics.med-ph], [SDV.CAN]Life Sciences [q-bio]/Cancer
Abstract

International audience; Aim/Introduction: Patients with unresectable neuroendocrine tumors can be treated by internal radiation therapy administrating 7.4 GBq of Lu177-labeled somatostatin analog. Image-based absorbed dose estimation to organs requires several time-points acquisitions but their number is limited by clinical and logistical constraints. The goal of this work was to propose an adaptive dosimetric workflow for estimating absorbed doses from a reduced number of acquisitionacquisitionMaterials and Methods: Thirteen patients with neuroendocrine tumors received four injections of 7.4 GBq of 177Lu-DOTATATE except one patient because of haematological toxicity. Three SPECT/CT acquisitions were performed after the first cycle at 1h, 24h and 96h or 144h and a single acquisition after three latter cycles at 24h for estimating absorbed doses by kidneys, liver and intrahepatic tumors, spleen and bone marrow. Monte Carlo simulations were used to estimate dose rates for each acquisition to take into account self- and cross- doses. These dose rates were then integrated at the organ level (ODR). Time dose rate curves (TDC) were fitted with a tri-exponential function to respect the patients' physiology. For cycles with less than three acquisitions, three methods were proposed and evaluated. If the acquisition at 24h of cycle 1 was missing, the ODR was approximated by the next first ODR at 24h scaled according to the injected activities at each cycle. When only one acquisition was available, two cases were distinguished. Absorbed doses were estimated from the TDC of a previous cycle for the samepatient scaled to the ODR available. Otherwise, TDC of other patients were scaled to the ODR and their integrals were averaged to obtain absorbed doses as proposed by Jackson and al. (1) with time activity curvesResults: Estimated errors when the ODR at 24h was replaced was inferior to 15.9% except for one patient. With only one acquisition, the lowest error was obtained for acquisitions at 96h or 144h with two methods: less than 11% for liver and spleen and less than 20% for kidneys. Thesemethods are being tested for bone marrow dose estimation. Absorbed doses estimated with this workflow were: 2⼟.50.9 Gy (left kidney), 2.7⼟1.2 Gy (right kidney), 2.8⼟1.7 Gy (liver) and 3.4⼟1.6 Gy (spleen) all cycles taken together. Conclusion: The proposed workflow allows the estimation of organ doses from a reduced number of acquisitions for patients treated with 177Lu. References: (1) Jackson and al. JNM 2020

Published
2021

22. Comparison of intraoperative functional brain map obtained with RGB imaging with preoperative functional brain map obtained with functional magnetic Resonance imaging

Author

Josset, Anne, Caredda, C., Van Reeth, Eric, Mahieu-Williame, L., Sablong, Raphaël, Sdika, Michaël, Schneider, Fabien C., Guyotat, Jacques, Montcel, Bruno, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Plateforme d'Imagerie Multimodale LyonTech (PILoT), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre Hospitalier Universitaire de Saint-Etienne (CHU de Saint-Etienne), Hospices Civils de Lyon (HCL), Caredda, Charly, Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2021

23. Comparison of efficiency and activity recovery coefficients of Compton and Anger cameras in Nuclear Medicine

Author

Etxebeste, Ane, Muñoz, Enrique, Dauvergne, Denis, Letang, J M, Borja-Lloret, M., Llosá, G, Maxim, Voichita, Sarrut, David, Testa, Etienne, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Physique des 2 Infinis de Lyon (IP2I Lyon), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Université Grenoble Alpes (UGA)

Subjects
[PHYS.PHYS.PHYS-MED-PH]Physics [physics]/Physics [physics]/Medical Physics [physics.med-ph], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2021

24. Characterizing interactions between cardiac shape and deformation by non-linear manifold learning

Author

Duchateau Nicolas, Di Folco Maxime, Moceri Pamela, Clarysse Patrick, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Centre Hospitalier Universitaire de Nice (CHU Nice)

Subjects
Computer science, Population, Diffusion map, Health Informatics, 030204 cardiovascular system & hematology, 03 medical and health sciences, 0302 clinical medicine, manifold learning, information fusion, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Radiology, Nuclear Medicine and imaging, education, 030304 developmental biology, dimensionality reduction, 0303 health sciences, Manifold alignment, education.field_of_study, Multiple kernel learning, Radiological and Ultrasound Technology, business.industry, Dimensionality reduction, Nonlinear dimensionality reduction, Heart, Pattern recognition, Computer Graphics and Computer-Aided Design, Manifold, myocardial strain, Embedding, Computer Vision and Pattern Recognition, Artificial intelligence, business, Cardiac imaging, Algorithms
Abstract

International audience; In clinical routine, high-dimensional descriptors of the cardiac function such as shape and deformation are reduced to scalars (e.g. volumes or ejection fraction), which limit the characterization of complex diseases. Besides, these descriptors undergo interactions depending on disease, which may bias their computational analysis. In this paper, we aim at characterizing such interactions by unsupervised manifold learning. We propose to use a sparsified version of Multiple Manifold Learning to align the latent spaces encoding each descriptor and weighting the strength of the alignment depending on each pair of samples. While this framework was up to now only applied to link different datasets from the same manifold, we demonstrate its relevance to characterize the interactions between different but partially related descriptors of the cardiac function (shape and deformation). We benchmark our approach against linear and non-linear embedding strategies, among which the fusion of manifolds by Multiple Kernel Learning, the independent embedding of each descriptor by Diffusion Maps, and a strict alignment based on pairwise correspondences. We first evaluated the methods on a synthetic dataset from a 0D cardiac model where the interactions between descriptors are fully controlled. Then, we transfered them to a population of right ventricular meshes from 310 subjects (100 healthy and 210 patients with right ventricular disease) obtained from 3D echocardiography, where the link between shape and deformation is key for disease understanding. Our experiments underline the relevance of jointly considering shape and deformation descriptors, and that manifold alignment is preferable over fusion for our application. They also confirm at a finer scale the characteristic traits of the right ventricular diseases in our population.

Published
2021

25. Comparison of Fitting Approaches for Diffusion-weighted MRI Models in Liver Simulation

Author

Huang, Jiqing, Ratiney, H., Leporq, Benjamin, beuf, olivier, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2021

26. CONTROLE OPTIMAL APPLIQUE A L'ELASTOGRAPHIE PAR RM MULTIFREQUENCE SIMULTANEE

Author

Sango Solanas, Pilar, Tse-Ve-Koon, Kevin, Van Reeth, Eric, CAUSSY, Cyrielle, beuf, olivier, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Cardiovasculaire, métabolisme, diabétologie et nutrition (CarMeN), Université de Lyon-Université de Lyon-Hospices Civils de Lyon (HCL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Sango Solanas, Pilar, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2021

27. Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients

Author

Michel Dojat, Verónica Muñoz-Ramírez, Florence Forbes, Nicolas Pinon, Carole Lartizen, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension (STATIFY), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Pinon, Nicolas

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer science, Parkinson's disease, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Anomaly detection, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 030304 developmental biology, Auto-encoders, 0303 health sciences, Artificial neural network, business.industry, Anomaly (natural sciences), [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, Image and Video Processing (eess.IV), Pattern recognition, Electrical Engineering and Systems Science - Image and Video Processing, Siamese networks, Identification (information), Patches, Brain lesions, Artificial intelligence, business, 030217 neurology & neurosurgery, Image based
Abstract

International audience; Although neural networks have proven very successful in a number of medical image analysis applications, their use remains difficult when targeting subtle tasks such as the identification of barely visible brain lesions, especially given the lack of annotated datasets. Good candidate approaches are patch-based unsupervised pipelines which have both the advantage to increase the number of input data and to capture local and fine anomaly patterns distributed in the image, while potential inconveniences are the loss of global structural information. We illustrate this trade-off on Parkinson's disease (PD) anomaly detection comparing the performance of two anomaly detection models based on a spatial auto-encoder (AE) and an adaptation of a patch-fed siamese auto-encoder (SAE). On average, the SAE model performs better, showing that patches may indeed be advantageous.

Published
2021

28. Intraoperative Resting-State Functional Connectivity Based on RGB Imaging

Author

Michaël Sdika, Raphaël Sablong, Bruno Montcel, Laurent Mahieu-Williame, J Guyotat, C. Caredda, Fabien C Schneider, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Plateforme d'Imagerie Multimodale LyonTech (PILoT), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre Hospitalier Universitaire de Saint-Etienne (CHU de Saint-Etienne), and Hospices Civils de Lyon (HCL)

Subjects
Medicine (General), intraoperative imaging, Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Clinical Biochemistry, Article, 030218 nuclear medicine & medical imaging, Resection, 03 medical and health sciences, optical imaging, 0302 clinical medicine, Optical imaging, R5-920, resting-state, medicine, Intraoperative imaging, medicine.diagnostic_test, Resting state fMRI, Functional connectivity, functional connectivity, Magnetic resonance imaging, RGB imaging, Neuronal activation, RGB color model, 030217 neurology & neurosurgery, Biomedical engineering
Abstract

RGB optical imaging is a marker-free, contactless, and non-invasive technique that is able to monitor hemodynamic brain response following neuronal activation using task-based and resting-state procedures. Magnetic resonance imaging (fMRI) and functional near infra-red spectroscopy (fNIRS) resting-state procedures cannot be used intraoperatively but RGB imaging provides an ideal solution to identify resting-state networks during a neurosurgical operation. We applied resting-state methodologies to intraoperative RGB imaging and evaluated their ability to identify resting-state networks. We adapted two resting-state methodologies from fMRI for the identification of resting-state networks using intraoperative RGB imaging. Measurements were performed in 3 patients who underwent resection of lesions adjacent to motor sites. The resting-state networks were compared to the identifications provided by RGB task-based imaging and electrical brain stimulation. Intraoperative RGB resting-state networks corresponded to RGB task-based imaging (DICE:0.55±0.29). Resting state procedures showed a strong correspondence between them (DICE:0.66±0.11) and with electrical brain stimulation. RGB imaging is a relevant technique for intraoperative resting-state networks identification. Intraoperative resting-state imaging has several advantages compared to functional task-based analyses: data acquisition is shorter, less complex, and less demanding for the patients, especially for those unable to perform the tasks.

Published
2021

29. Description de points clés par apprentissage dans des images médicales 3D

Author

Loiseau--Witon, Nicolas, Kechichian, Razmig, Valette, Sébastien, Bartoli, Adrien, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Institut Pascal (IP), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Imagerie Tomographique et Radiothérapie, Centre National de la Recherche Scientifique [CNRS], Sciencesconf.org, CCSD, Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Réseaude neurones convolutionnel, Perte par triplet, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO]Computer Science [cs], [INFO] Computer Science [cs], Points clés, Imagerie médicale
Abstract

International audience; Nous présentons une nouvelle approche pour apprendre des descripteurs de points clés en 3D, que nous appliquons aux images médicales scanners médicaux corps entier. Il a été démontré que les descripteurs de points clés basés sur les Réseaux de Neurones Convolutifs ( RNC) donnent de meilleurs résultats que les descripteurs faits à la main, pour les images 2D. L’adaptation aux images 3D telles que les images tomodensitométriques n’est cependant pas simple, essentiellement en raison du manque de données d’entraînement labellisées.Nous proposons de générer des données d’entraînement semi-synthétiques. L’idée principale est d’estimer d’abord la densité des transformations locales entre les patients à partir d’un petit nombre d’images tomodensitométriques dont les correspondances entre des points de repère anatomique, définis par des experts, sont connues. Nous échantillonnons ensuite un grand nombre de transformations à partir de cette densité et transformons des volumes labellisés, pour lesquels nous pouvons ensuite former des correspondances de points clés exactes en utilisant une correspondance guidée par la transformation. Notre fonction de description est un RNC à deux étages, que nous formons en utilisant la perte par triplets inspirée par l’apprentissage de descripteurs 2D, avec une extraction de triplets en ligne.Nos résultats expérimentaux montrent que notre descripteur appris surpasse le descripteur 3D-SURF créé à la main dans une évaluation sur les données semi- synthétiques ainsi qu’en recalage d’images 3D réelles, avec un temps d’exécution similaire

Published
2021

30. 3D Clifford Analytic Signal for 3D Envelope Detection on Ultrasound Volume

Author

Philippe Delachartre, Liang Wang, Patrick Girard, Patrick Clarysse, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon)

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer science, Demodulation, Iterative reconstruction, Analytic signal, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm, Signal, Peak signal-to-noise ratio, ComputingMilieux_MISCELLANEOUS, Imaging phantom, Envelope detector, Envelope (waves)
Abstract

Envelope detection is one of the essential steps for ultrasound B-mode image reconstruction. Typically, this reconstruction is based on one dimensional (1D) radio frequency (RF) signal demodulation. In the literature, 1D envelopes are usually stacked to achieve two or three dimensional (2D or 3D) envelope detection, which leads to possible losses of multidimensional context information. In this paper, we propose a hypercomplex analytical signal in the form of Clifford biquater-nion, which is called 3D Clifford analytic signal (3D CAS). Firstly, the convolution is utilized to define the 3D CAS in terms of biquaternion. Next, the relation between the 3D CAS and the classical Hahn's analytical signals in the 3D case is illustrated with the help of Hilbert transforms, which enables a reliable numerical implementation of 3D CAS. Then, the modulus of 3D CAS is proposed and applied to a straightforward 3D ultrasonic RF envelope detection by taking into consideration the information of amplitude of all three directions of space. Experiments were carried out on the ultrasound volume from a phantom with a biopsy needle inserted. The experiments provide a better visual experience from envelope images of RF volume than the compared 1D and 2D methods. The 3D and 2D envelopes present 36.5% and 15.2% improvement to 1D, respectively, from the contrast to noise ratio between the needle region and the adjacent background. From an independent public 2D liver tumor RF sequences of 29 mice, the average structural index similarity (SSIM) and peak signal to noise ratio (PSNR) were improved by 53.0% and 39.0%, respectively, from 2D envelopes comparing to 1D envelopes.

Published
2021

31. Semi-supervised annotation of Transcranial Doppler ultrasound micro-embolic data

Author

Philippe Delachartre, Yamil Vindas, Marilys Almar, Blaise Kevin Guepie, Emmanuel Roux, Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Modélisation et Sûreté des Systèmes (LM2S), Laboratoire Informatique et Société Numérique (LIST3N), and Université de Technologie de Troyes (UTT)-Université de Technologie de Troyes (UTT)

Subjects
Computer science, business.industry, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Pattern recognition, Semi-supervised learning, Time saving, Transcranial Doppler, Projection (relational algebra), Annotation, Data information, ComputingMethodologies_PATTERNRECOGNITION, Manual annotation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Spectrogram, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS
Abstract

Transcranial Doppler (TCD) is a non-invasive ultrasound monitoring technique allowing real time measurements of the blood flow velocity mainly in the Middle Cerebral Artery. It is commonly used to monitor patients with stroke risk by detecting micro-emboli. This technique generates a considerable amount of data whose annotation is expensive and time-consuming. We propose a semi-supervised learning method to annotate a dataset of micro-embolic data with reduced requirements of manual annotation. First, we start by extracting features from TCD spectrograms in an unsupervised manner using an Auto-encoder. Then, we project those features in a 2D space using the t-SNE algorithm. Afterwards, the dataset is partially annotated by an expert based on the 2D projection and the overall data information. Finally, the labels of the annotated samples are propagated to a part of the unlabeled samples using a K-nearest-neighbors (KNN) strategy. We evaluate our annotation method through the annotation error and the percentage of labeled samples with respect to the unlabeled samples. Our results show that we are able to annotate all the unlabeled samples with an annotation error of 12 % in less than 1 second against around 2 hours for a human expert. This represents a time saving of a factor of 105, showing the interest of our method.

Published
2021

32. Evaluation of simulators for x-ray speckle-based phase contrast imaging

Author

Max Langer, Jean Michel Létang, Laurène Quénot, Emmanuel Brun, Synchrotron Radiation for Biomedicine = Rayonnement SynchroTROn pour la Recherche BiomédicalE (STROBE), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Rayet, Béatrice

Subjects
Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Monte Carlo method, X-ray Phase Contrast Imaging, 01 natural sciences, 030218 nuclear medicine & medical imaging, 010309 optics, 03 medical and health sciences, Speckle pattern, 0302 clinical medicine, Optics, Speckle based Imaging, 0103 physical sciences, Radiology, Nuclear Medicine and imaging, Computer Simulation, Monte-Carlo Simulations, Radiological and Ultrasound Technology, business.industry, Scattering, X-Rays, Phase-contrast imaging, Experimental data, Ranging, Refraction, Radiography, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, X-Ray Phase-Contrast Imaging, business, Monte Carlo Method, Synchrotrons
Abstract

International audience; X-ray phase contrast imaging (PCI) denotes a group of highly sensitive imaging techniques that permits imaging at scales ranging from nanoscopic to the medical. Recently introduced, speckle-based imaging has seen a rapid development because of its experimental simplicity and its capability to retrieve the refraction, the scattering and the absorption of a sample using a conventional X-ray set-up. Precise simulation would permit to optimise the imaging setups for different applications, but until now works on simulation of X-ray speckle-based phase contrast imaging have been very few. In this work we evaluate different simulation codes, based on Monte-Carlo, analytical ray-tracing and wave-optics Fresnel propagation. The simulation results are compared to both synchrotron and conventional imaging experiments to permits their validation. We obtain a strong similarity between simulated and experimental data. We discuss the validity and applicability of each approach.

Published
2021

33. GAN-Based Synthetic FDG PET Images from T1 Brain MRI Can Serve to Improve Performance of Deep Unsupervised Anomaly Detection Models

Author

Julien Jung, Carole Lartizien, Daria Zotova, Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Lartizien, Carole, Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de recherche en neurosciences de Lyon (CRNL), and Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Cycle-GAN, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Unsupervised Learning, PET MRI, CAD, 02 engineering and technology, Synthetic data, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0202 electrical engineering, electronic engineering, information engineering, Brain mri, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Sensitivity (control systems), Lesion detection, business.industry, 020206 networking & telecommunications, Pattern recognition, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], lesion detection, Unsupervised learning, 020201 artificial intelligence & image processing, Anomaly detection, Artificial intelligence, business
Abstract

International audience; Research in cross-modal translation or synthesis domain has been very productive over the past few years to tackle the scarce availability of large curated datasets for the training of deep models, with promising performance of GAN-based architectures. However, only a few of these studies assessed task-based related performance of these synthetic data. In this work, we design and compare different GAN-based frameworks for generating synthetic brain FDG-PET images from T1weighted MRI data, and explore further impact of adding these fake PET data in the training of a deep brain anomaly detection model. Qualitative and quantitative results allow us to conclude that the generated PET images look similar to real ones with SSIM and PSNR values around 0.88 and 23.5 respectively for the best GAN architecture. Training of the brain anomaly detection model on hybrid datasets including 35 real and 40 synthetic FDG PET data, allows achieving a 65% detection sensitivity of subtle epilepsy lesions in 17 real PET exams of patients, while the sensitivity is 53% when training with the 35 real PET exams only, thus demonstrating the diagnostic value of these synthetic data for the design of CAD models.

Published
2021

34. Results and interpretation of a fitting challenge for MR spectroscopy set up by the MRS study group of ISMRM

Author

Marjańska, Małgorzata, Deelchand, Dinesh, Kreis, Roland, Alger, Jeffry, Bolan, Patrick, Borbath, Tamas, Boumezbeur, Fawzi, Fernandes, Carolina, Coello, Eduardo, Nagraja, Bharath Halandur, Považan, Michal, Ratiney, Hélène, Sima, Diana, Starčuková, Jana, Soher, Brian, Wilson, Martin, Asten, Jack J.A., RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Accuracy and precision, Basis (linear algebra), Synthetic data, 030218 nuclear medicine & medical imaging, Interpretation (model theory), Set (abstract data type), 03 medical and health sciences, 0302 clinical medicine, Robustness (computer science), Fitting methods, Brain concentrations, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Radiology, Nuclear Medicine and imaging, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Algorithm, 030217 neurology & neurosurgery, ComputingMilieux_MISCELLANEOUS, Mathematics
Abstract

PURPOSE Fitting of MRS data plays an important role in the quantification of metabolite concentrations. Many different spectral fitting packages are used by the MRS community. A fitting challenge was set up to allow comparison of fitting methods on the basis of performance and robustness. METHODS Synthetic data were generated for 28 datasets. Short-echo time PRESS spectra were simulated using ideal pulses for the common metabolites at mostly near-normal brain concentrations. Macromolecular contributions were also included. Modulations of signal-to-noise ratio (SNR); lineshape type and width; concentrations of γ-aminobutyric acid, glutathione, and macromolecules; and inclusion of artifacts and lipid signals to mimic tumor spectra were included as challenges to be coped with. RESULTS Twenty-six submissions were evaluated. Visually, most fit packages performed well with mostly noise-like residuals. However, striking differences in fit performance were found with bias problems also evident for well-known packages. In addition, often error bounds were not appropriately estimated and deduced confidence limits misleading. Soft constraints as used in LCModel were found to substantially influence the fitting results and their dependence on SNR. CONCLUSIONS Substantial differences were found for accuracy and precision of fit results obtained by the multiple fit packages.

Published
2021

35. Comparison of high-resolution magnetic resonance imaging and micro-computed tomography arthrography for in-vivo assessment of cartilage in non-human primate models

Author

Olivier Beuf, Laurent Mahieu-Williame, T. Chuzel, Denis Grenier, Emmanuel Chereul, Anne-Laure Perrier, F. Taborik, Hugues Contamin, Kevin Tse Ve Koon, L. Magnier, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Plateforme d'Imagerie Multimodale LyonTech (PILoT), CYNBIOSE, Laboratoire Optimisation de la Conception et Ingénierie de l'Environnement (LOCIE), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), VOXCAN, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Tse Ve Koon, Kevin

Subjects
030203 arthritis & rheumatology, medicine.diagnostic_test, Computer science, Cartilage, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, 030209 endocrinology & metabolism, Magnetic resonance imaging, Context (language use), Image processing, Osteoarthritis, Knee Joint, medicine.disease, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, medicine, Medical imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Radiology, Nuclear Medicine and imaging, Tomography, ComputingMilieux_MISCELLANEOUS, Biomedical engineering
Abstract

Background Non-human primate (NHP) could be an interesting model for osteoarthritis (OA) longitudinal studies but standard medical imaging protocols are not able to acquire sufficiently high-resolution images to depict the thinner cartilage (compared to human) in an in vivo context. The aim of this study was thus to develop and validate the acquisition protocols for knee joint examination of NHP using magnetic resonance imaging (MRI) at 1.5 T and X-ray micro-computed tomography arthrography (µCTA). Methods The first phase of the study focused on developing dedicated in vivo HR-MRI and µCTA protocols for simultaneous acquisitions of both knee joints on NHP. For MR, a dedicated two-channel receiver array coil and acquisition sequence were developed on a 1.5 T Siemens Sonata system and tuned to respect safety issues and reasonable examination time. For µCTA, an experimental setup was devised so as to fulfill similar requirements. The two imaging protocols were used during a longitudinal study so as to confirm that repeated injections of loxaglic acid (contrast agent used for µCTA) didn't induce any bias in cartilage assessment and to compare segmentation results from the two modalities. Lateral and medial cartilage tibial plateaus were assessed using a common image processing protocol leading to a 3D estimation of the cartilage thickness. Results From HR-MRI and µCTA images, thickness distributions were extracted allowing for proper evaluation of knee cartilage thickness of the primates. Results obtained in vivo indicated that the µCTA protocol did not induce any bias in the measured cartilage parameters and moreover, segmentation results obtained from the two imaging modalities were consistent. Conclusions MR and µCTA are valuable imaging tools for the morphological evaluation of cartilage in NHP models which in turn can be used for OA studies.

Published
2021

36. Electromagnetic Simulation of Signal Distribution of Various RF Endoluminal Loop Geometries with Coil Orientation: Towards a Reconfigurable Design

Author

Olivier Beuf, Kevin Tse Ve Koon, Fraser Robb, Henri Souchay, Hamza Raki, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), General Electric Medical Systems [Buc] (GE Healthcare), General Electric Medical Systems, General Electric Healthcare, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Tse Ve Koon, Kevin

Subjects
Physics, Article Subject, Radiological and Ultrasound Technology, Orientation (computer vision), [SPI.ELEC] Engineering Sciences [physics]/Electromagnetism, Acoustics, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Radius, Decoupling (cosmology), Signal, [SPI.TRON] Engineering Sciences [physics]/Electronics, Standard deviation, 030218 nuclear medicine & medical imaging, [SPI.TRON]Engineering Sciences [physics]/Electronics, 03 medical and health sciences, [SPI.ELEC]Engineering Sciences [physics]/Electromagnetism, 0302 clinical medicine, Sampling (signal processing), Region of interest, Electromagnetic coil, 030220 oncology & carcinogenesis, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Radiology, Nuclear Medicine and imaging, Physical and Theoretical Chemistry, Spectroscopy
Abstract

With the objective of improving MR endoluminal imaging of the colonic wall, electromagnetic simulations of different configurations of single-layer and double-layer, and double-turn endoluminal coil geometries were run. Indeed, during colon navigation, variations in coil orientation with respect to B0 are bound to occur, leading to impaired image acquisition due to a loss of signal uniformity. In this work, three typical coil orientations encountered during navigation were chosen and the resulting signal uniformity of the different geometries was investigated through the simulatedB1x,y/IRtvalues. Sampling this quantity over a circle of radius r enabled us to calculate the coefficient of variation (= standard deviation/mean) for this given distance. This procedure was repeated forr∈5;15 mm, which represents the region of interest in the colon. Our results show that single-loop and double-layer geometries could provide complementary solutions for improved signal uniformity. Finally, using four microelectromechanical system switches, we proposed the design of a reconfigurable endoluminal coil able to switch between those two geometries while also ensuring the active decoupling of the endoluminal coil during the RF transmission of an MR experiment.

Published
2021

37. Learning a reconnecting regularization term for blood vessel variational segmentation

Author

Antoine Vacavant, Sophie Carneiro Esteves, Odyssée Merveille, Institut Pascal (IP), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Carneiro Esteves, Sophie, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Modality (human–computer interaction), Generalization, business.industry, Computer science, Deep learning, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, deep learning, Image segmentation, Machine learning, computer.software_genre, Data type, Regularization (mathematics), Term (time), blood vessel segmentation, reconnection, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, variational approach, Segmentation, Artificial intelligence, business, computer
Abstract

International audience; The segmentation of blood vessels in medical images is a challenging task as they are thin, connected and tortuous. The detection of a connected vascular network is of the utmost importance in clinical applications (e.g. blood flow simulations, vascular network modeling and analysis). Deep learning approaches have been developed to tackle this issue, but they require a large annotated dataset for each new application of interest, which is very challenging to build for vascular networks. In this work, rather than learning the segmentation task, we propose to learn a reconnecting regularization term that learns geometric properties of vascular networks independent of the image modality. Therefore, this term generalizes better than deep learning segmentation models, and can be easily plugged into variational segmentation frameworks to detect vascular networks in different datasets without requiring annotations. We apply this approach on retinal images by training our reconnecting term on the STARE dataset and applying it on the DRIVE dataset. We show that our approach better preserves the connectivity of vascular networks than classic regularization terms in the literature. Finally, we illustrate the generalization power of our reconnecting term by applying it to other types of data.

Published
2021

38. Region-of-Interest CT Reconstruction using One-Endpoint Hilbert Inversion in Optimized Directions

Author

Coussat, Aurélien, Rit, Simon, Defrise, Michel, Létang, Jean Michel, Létang, Jean, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL)

Subjects
Computer Science::Computer Vision and Pattern Recognition, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging
Abstract

International audience; In computed tomography, scanning the entire object is sometimes impossible, causing truncated projection data. Reconstruction is however still possible: using differentiated backprojection, the Hilbert transform of the object can be calculated and inverted along line segments in the field-of-view. When two endpoints of the line segment are outside the object extent, a stable analytic reconstruction formula exists. When only one endpoint is outside the object extent, every pixel can be reconstructed, but no inversion formula is known yet. Uniqueness of the inverse Hilbert transform is nevertheless guaranteed along such segments, and a numerical inverse can be used. Most pixels of the field-of-view accept more than one "one-endpoint line segment" and one can choose the direction; we propose here to select the optimal direction based on an empirical criterion. Image quality improvement is assessed against a reconstruction that uses a single direction for every pixel.

Published
2021

39. Implementation of the virtual fan-beam method for 2D region-of-interest reconstruction from truncated data

Author

Charles, Mathurin, Clackdoyle, Rolf, Rit, Simon, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[INFO.INFO-IM]Computer Science [cs]/Medical Imaging
Abstract

International audience; In the context of two-dimensional (2D) image reconstruction from truncated projections, we describe five implementations, each based on a different formula derived from the virtual fan-beam (VFB) method. Three formulae are already known: (a) and (b) perform the back-projection in the parallel-beam geometry and (c) performs the back-projection in the virtual fan-beam geometry. Two new formulae, (d) and (e), perform the back-projection in the acquisition geometry. Our simulation results using the Shepp-Logan phantom suggest that the best accuracy is obtained from the implementations of formulae (b), (d) and (e).

Published
2021

40. MODULATION ELECTRO-OPTIQUE POUR LA TRANSMISSION DU SIGNAL EN IRM PRECLINIQUE A 7T

Author

Nobre, Paul, Gaborit, Gwenaël, Sablong, Raphaël, Courjal, Nadège, Behague, Florent, Coste, Antoine, Godet, Adrien, Suarez, Miguel, Duvillaret, Lionel, Beuf, Olivier, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Microélectronique, Electromagnétisme et Photonique - Laboratoire d'Hyperfréquences et Caractérisation (IMEP-LAHC), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Kapteos SAS, Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), and Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SPI.ELEC]Engineering Sciences [physics]/Electromagnetism, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, capteur, [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, modulateur, Radiofréquence, IRM
Abstract

International audience; L'objectif de ces travaux est d'utiliser le principe de la conversion électro-optique pour remplacer des câbles coaxiaux en IRM. Cette étude présente une comparaison d'images obtenues par transmission optique ou galvanique du signal, puis les caractéristiques du modulateur utilisé ainsi que celles d'un modulateur sans tension de biais qui permettrait le développement d'un capteur passif à liaison optique avec modulateur embarqué.

Published
2021

41. PyPhase – a Python package for X-ray phase imaging

Author

LANGER, Max, Zhang, Yuhe, Figueirinhas, Diogo, Forien, Jean-Baptiste, Mom, Kannara, Mouton, Claire, Mokso, Rajmund, Villanueva-Perez, Pablo, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; X-ray propagation-based imaging techniques are well established at synchrotron radiation and laboratory sources. However, most reconstruction algorithms for such image modalities, also known as phase-retrieval algorithms, have been developed specifically for one instrument by and for experts, making the development and diffusion of such techniques difficult. Here, PyPhase , a free and open-source package for propagation-based near-field phase reconstructions, which is distributed under the CeCILL license, is presented. PyPhase implements some of the most popular phase-retrieval algorithms in a highly modular framework supporting its deployment on large-scale computing facilities. This makes the integration, the development of new phase-retrieval algorithms, and the deployment on different computing infrastructures straightforward. Its capabilities and simplicity are presented by application to data acquired at the synchrotron source MAX IV (Lund, Sweden).

Published
2021

42. Cartographie des lipides intra et extra myocellulaires (IMCL et EMCL) par imagerie spectroscopiquespirale de résonance magnétique à 3T

Author

Naëgel, Antoine, Karkouri, Jabrane, Viallon, Magalie, Ratiney, Hélène, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Siemens Healthineers [Saint-Denis], Wolfson Brain Imaging Center, and University of Cambridge [UK] (CAM)

Subjects
Spiral Spectroscopic Imaging, IMCL, Skeletal Muscle, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, lipides intramyocellulaires, muscle squelettique, intramyocellular lipids, Magnetic Resonance Imaging, Imagerie spectroscopique par résonance magnétique, Magnetic Resonance Spectroscopic Imaging, Imagerie par résonance magnétique, Imagerie spectroscopique spirale, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; We propose a simple and rapid method for processing spectroscopic imaging data to map intra- and extra-myocellular lipids (IMCL and EMCL).; Nous proposons une méthode simple et rapide de traitement des données d’imagerie spectroscopique pour cartographier les lipides intra et extra myocellulaires (IMCL et EMCL).

Published
2021

43. Multiplane deconvolution in underwater acoustics: Simultaneous estimations of source level and position

Author

Barbara Nicolas, Maxime Polichetti, Valentin Baron, Jerome Mars, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), MicrodB, Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), ANR-10-LABX-0060,CeLyA,Lyon Acoustics Centre(2010), ANR-16-IDEX-0005,IDEXLYON,IDEXLYON(2016), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Nicolas, Barbara, Lyon Acoustics Centre - - CeLyA2010 - ANR-10-LABX-0060 - LABX - VALID, and IDEXLYON - - IDEXLYON2016 - ANR-16-IDEX-0005 - IDEX - VALID

Subjects
Pulmonary and Respiratory Medicine, Beamforming, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Plane (geometry), Acoustics, Signal Processing, Computer-Assisted, Source level, 01 natural sciences, 010305 fluids & plasmas, Noise, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Position (vector), 0103 physical sciences, Pediatrics, Perinatology and Child Health, Computer Simulation, 14. Life underwater, Deconvolution, Underwater, Underwater acoustics, 010301 acoustics, ComputingMilieux_MISCELLANEOUS
Abstract

International audience; In many acoustic imaging applications, conventional beamforming (CBF) cannot provide both accurate position and source level estimates simultaneously. Also, the CBF acoustic maps suffer from many artifacts due to the spreading of large point-spread-functions. An original CLEAN deconvolution procedure, including an additional plane containing out-of-plane interfering sources, is proposed here to achieve simultaneous localization, source level estimation, and de-noising. The approach is illustrated using experimental data mimicking a challenging deep-sea mining configuration: an underwater acoustic source of interest is located 700 m below the sea surface, tens of meters from a 3 m-length array, with boat noise as the disturbing source.

Published
2021

44. Hyperquaternion Conformal Groups

Author

Romaric Pujol, Patrick Clarysse, Robert Goutte, Patrick Girard, Philippe Delachartre, Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Pure mathematics, Mathematics Subject Classification (2010).Primary 15A66, Secondary 11E88, 15A67, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Conformal field theory, Applied Mathematics, Clifford algebra, Subalgebra, Conformal map, Hyperquaternions, 01 natural sciences, Conformal Groups, Conformal group, Tensor product, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Canonical Decomposition, 0103 physical sciences, 010307 mathematical physics, Quaternions, 010306 general physics, Quaternion, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Conformal geometry, Mathematics
Abstract

The paper gives a new representation of conformal groups in n dimensions in terms of hyperquaternions defined as tensor products of quaternion algebras (or a subalgebra thereof). Being Clifford algebras, hyperquaternions provide a good representation of pseudo-orthogonal groups such as $$O(p+1,q+1)$$ isomorphic to the nD conformal group with $$n=p+q.$$ The representation yields simple expressions of the generators, independently of matrices or operators. The canonical decomposition and the invariants are discussed. As application, the 4D relativistic conformal group is detailed together with a worked example. Finally, the formalism is compared to the operator representation. Potential uses include in particular, conformal geometry, computer graphics and conformal field theory.

Published
2021

45. Learning 3D Medical Image Patch Descriptors with the Triplet Loss

Author

Loiseau-Witon, N, Kéchichian, Razmig, Valette, Sebastien, Bartoli, Adrien, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Imagerie Tomographique et Radiothérapie, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Institut Pascal (IP), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Valette, Sébastien, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon)

Subjects
[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Triplet loss, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Medical imaging, Convolution Neural Network
Abstract

International audience; Computational anatomy focuses on the analysis of the human anatomical variability. Typical applications are the discovery of differences across healthy and sick subjects and the classification of anomalies. A fundamental tool in computational anatomy, which forms the central focus of this paper, is the computation of point correspondences across volumes (3D images) such as Computed Tomography (CT) volumes, for multiple subjects. More specifically, we consider automatically detected keypoints and their local descriptors, computed from the image or volume patch surrounding each keypoint. Theses descriptors are essential because they must be discriminant and repeatable [5,10]. Learned descriptors based on Convolutional Neural Networks (CNN) have recently shown great success for 2D images [4]. However, while classical 2D image descriptors were extended to volumes [1], recent learning-based approaches have been limited to 2D detection and description. The extension to 3D descriptors was only proposed in [6], in the context of image retrieval. We propose a methodology to construct these learned volume keypoint descriptors. The main difficulty is to define a sound training approach, combining a training dataset and a loss function. In short, we propose to generate semi-synthetic data by transforming real volumes and to use a triplet loss inspired by 2D descriptor learning. Our experimental results show that our learned descriptor outperforms the hand-crafted descriptor 3D-SURF [1], a 3D extension of SURF, with similar runtime.

Published
2021

46. Investigation of the impact of normalization on the study of interactions between myocardial shape and deformation

Author

Maxime Di Folco, Pamela Moceri, Patrick Clarysse, Nicolas Guigui, Nicolas Duchateau, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Côte d'Azur (UCA), Hôpital Cimiez [Nice] (CHU), ANR-19-CE45-0005,MIC-MAC,Modélisation de la hiérarchie entre descripteurs cardiaques par apprentissage automatique(2019), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), ANR-11-LABX-0063,PRIMES,Physique, Radiobiologie, Imagerie Médicale et Simulation(2011), and European Project: 786854,H2020 Pilier ERC,ERC AdG(2018)

Subjects
Normalization (statistics), heart shape, 030219 obstetrics & reproductive medicine, Computer science, business.industry, Heart shape, Nonlinear dimensionality reduction, food and beverages, Pattern recognition, 030204 cardiovascular system & hematology, Deformation (meteorology), Scale factor, 03 medical and health sciences, 0302 clinical medicine, manifold learning, Myocardial strain, symbols, myocardial strain, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, symbols.heraldic_charge, Artificial intelligence, business, Cardiac imaging
Abstract

International audience; Myocardial shape and deformation are two relevant descriptors for the study of cardiac function and can undergo strong interactions depending on diseases. Manifold learning provides low dimensional representations of these high-dimensional descriptors, but the choice of normalization can strongly affect the analysis. Besides, whether the shape normalization should include a scale factor is still an open question. In this paper, we investigate the influence of normalization choices on the study of the interactions between cardiac shape and deformation using Multiple Manifold Learning, a dimensionality reduction method that considers inter-and intra-descriptors link between samples. By studying the main variations of two different shape normalizations (one including scaling, the other one not) we observed that the scaled normalization concentrates variations of a given physiological characteristic on only one mode. The influence of the associated choice of the deformation normalization was evaluated by quantifying differences between the estimated low-dimensional spaces (one for each choice against a combination of both), revealing potential analysis biases that may arise depending on such choices.

Published
2021

47. Multispectral Endoscopic Setup for Real Time Pathology Detection

Author

Caredda, Charly, Montcel, Bruno, Sablong, Raphaël, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[SDV]Life Sciences [q-bio]
Abstract

International audience; We present a multispectral endoscope using visible and near infrared illumination that can be easily adapted to flexible endoscopes. The results show that this device could be used for semi-quantitative detection of pathological tissues.

Published
2021

48. Intraoperative Resting State Functional Connectivity Based on RGB Imaging

Author

Charly Caredda, Laurent Mahieu-Williame, Raphaël Sablong, Michaël Sdika, Fabien C. Schneider, Jacques Guyotat, Bruno Montcel, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Plateforme d'Imagerie Multimodale LyonTech (PILoT), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre Hospitalier Universitaire de Saint-Etienne (CHU de Saint-Etienne), and Hospices Civils de Lyon, Departement de Neurologie (HCL)

Subjects
[SDV]Life Sciences [q-bio]
Abstract

International audience; We present the methodology for the intraoperative identification of resting state networks using RGB imaging. The results show a good correlation between the resting state and the brain areas identified by electrical brain stimulation.

Published
2021

49. Multiple Wavelength Excitation 5-ALA Induced PpIX Fluorescence Spectroscopy in Guided Neurosurgery for Improving Glioma Classification

Author

GAUTHERON, Arthur, Hébert, M, Mahieu-Williame, L, Guyotat, J, Montcel, B, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire Hubert Curien [Saint Etienne] (LHC), Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS)-Institut d'Optique Graduate School (IOGS), Plateforme d'Imagerie Multimodale LyonTech (PILoT), Hospices Civils de Lyon, Departement de Neurologie (HCL), and SPIE

Subjects
classification, Protoporphyrin IX, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, glioma, interventionnal imaging, [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, neurosurgery, fluorescence spectroscopy, 5-ALA, human brain
Abstract

International audience; We present a methodology for PpIX parameters estimation based on multiwavelength excitation. This method aims to distinguish healthy tissues from tumour margins. It reduces the absolute estimation error of low density margins by 39.9%.

Published
2021

50. Intraoperative Statistical Parametric Brain Mapping using RGB Imaging

Author

Caredda, Charly, Mahieu-Williame, Laurent, Sablong, Raphaël, Sdika, Michaël, Guyotat, Jacques, Montcel, Bruno, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Plateforme d'Imagerie Multimodale LyonTech (PILoT), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Hospices Civils de Lyon, Departement de Neurologie (HCL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Caredda, Charly

Subjects
010309 optics, [SDV] Life Sciences [q-bio], 03 medical and health sciences, 0302 clinical medicine, [SDV]Life Sciences [q-bio], 0103 physical sciences, 01 natural sciences, 030217 neurology & neurosurgery
Abstract

International audience; We present the methodology for the intraoperative pixel-wise identification of activated cortical areas using RGB imaging. The results indicate that RGB imaging could be a useful complement to the electrical brain stimulation.

Published
2021

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