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1. Incorporation of prior knowledge of the signal behavior into the reconstruction to accelerate the acquisition of MR diffusion data

Author

Abascal, Juan F P J, Desco, Manuel, and Parra-Robles, Juan

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Diffusion MRI measurements using hyperpolarized gases are generally acquired during patient breath hold, which yields a compromise between achievable image resolution, lung coverage and number of b-values. In this work, we propose a novel method that accelerates the acquisition of MR diffusion data by undersampling in both spatial and b-value dimensions, thanks to incorporating knowledge about the signal decay into the reconstruction (SIDER). SIDER is compared to total variation (TV) reconstruction by assessing their effect on both the recovery of ventilation images and estimated mean alveolar dimensions (MAD). Both methods are assessed by retrospectively undersampling diffusion datasets of normal volunteers and COPD patients (n=8) for acceleration factors between x2 and x10. TV led to large errors and artefacts for acceleration factors equal or larger than x5. SIDER improved TV, presenting lower errors and histograms of MAD closer to those obtained from fully sampled data for accelerations factors up to x10. SIDER preserved image quality at all acceleration factors but images were slightly smoothed and some details were lost at x10. In conclusion, we have developed and validated a novel compressed sensing method for lung MRI imaging and achieved high acceleration factors, which can be used to increase the amount of data acquired during a breath-hold. This methodology is expected to improve the accuracy of estimated lung microstructure dimensions and widen the possibilities of studying lung diseases with MRI.

Published
2017

2. 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

4. Material Decomposition in Spectral CT using deep learning: A Sim2Real transfer approach

Author

Abascal, Juan F P J, Ducros, Nicolas, 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 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), 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
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

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
2020

5. Technical Note: Relative proton stopping power estimation from virtual monoenergetic images reconstructed from dual‐layer computed tomography

Author

Landry, Guillaume, primary, Dörringer, Fabian, additional, Si‐Mohamed, Salim, additional, Douek, Philippe, additional, Abascal, Juan F. P. J., additional, Peyrin, Françoise, additional, Almeida, Isabel P., additional, Verhaegen, Frank, additional, Rinaldi, Ilaria, additional, Parodi, Katia, additional, and Rit, Simon, additional

Published
2019
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6. Décomposition en base de matériaux à partir de données issues du scanner spectral PIXSCAN-FLI

Author

Ducros, Nicolas, Pivot, Odran, Dupont, Mathieu, Létang, Jean Michel, Rit, Simon, Abascal, Juan F P J, Morel, Christian, Boursier, Yannick, Peyrin, Françoise, 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), 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), 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), Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), European Synchrotron Radiation Facility (ESRF), ANR-11-LABX-0063,PRIMES,Physique, Radiobiologie, Imagerie Médicale et Simulation(2011), 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
imagerie d’agents de contraste, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, décomposition en base de matériaux, CT spectral, méthode polynomiale, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Introduction : Le scanner X spectral est une évolution du scanner X conventionnel permettant de reconstruire en 3D la concentration des matériaux constituant l’objet imagé. Actuellement, seuls quelques prototypes pré cliniques ont été développés, cette technologie soulevant encore des verrous tant au niveau de l’instrumentation que du traitement des données. Nous présentons les premières images expérimentales obtenues dans le cadre d’une collaboration entre CREATIS et le CPPM.Méthodes :L’une des approches possibles pour obtenir les images 3D des matériaux consiste à décomposer les projections dans un premier temps, puis à effectuer une reconstruction tomographique classique pour chacun des matériaux décomposés. La décomposition des projections a été effectuée par la méthode d’étalonnage polynomial par commutation de la tension de source du tube à rayons X. Cette méthode repose sur l’hypothèse que la quantité inconnue de chaque matériau est liée au nombre de photons X comptés dans différentes plages énergétiques via une relation polynomiale. Ayant estimé les coefficients des polynômes, au préalable, à partir d’acquisitions faites sur des objets étalonnés, la décomposition est alors immédiate. Pour chaque angle de projection et chaque pixel du détecteur, la décomposition de l’image a été effectuée en considérant un polynôme d’ordre 2. Les projections décomposées sont ensuite traitées par un algorithme de rétroprojection filtrée 3D (implémentation RTK). Des données réelles ont été acquises sur le micro-CT PIXSCAN-FLI spectral développé au CPPM. Ce scanner intègre un détecteur à comptage de photons XPAD3 capable de sélectionner les photons en fonction de leur énergie. La source de rayons X est un MICROFOCUS Hamamatsu (150kV, 500 μA) avec une anode en tungstène. L’objet imagé est un fantôme numérique de souris (Digimouse) imprimé en 3D. Il est constitué de PMMA et d’aluminium, matériaux dont l’atténuation est proche de celle des tissus mous et des os.Résultats :Les premiers résultats obtenus sur données réelles sont encourageants. Ils montrent que le PMMA et l’aluminium peuvent être séparés dans le fantôme de petit animal considéré. Des travaux futurs permettront d’évaluer la robustesse de l’approche au bruit. Cette technique d’imagerie quantitative, qui de plus peut être couplée à l’utilisation de produits de contraste, offre de nombreuses perspectives pour l’imagerie diagnostique.Remerciements :Ce travail a été réalisé grâce au soutien financier du LABEX PRIMES (ANR-11-LABX-0063) de l’Université de Lyon, dans le cadre du programme «Investissements d’Avenir» (ANR-11-IDEX-0007). Il a en outre bénéficié d’une aide de l’état gérée par l’Agence Nationale pour la Recherche référencée (ANR-11-INBS-0006).

Published
2017

7. Material decomposition using the PIXSCAN-FLI spectral micro-CT

Author

Ducros, Nicolas, Pivot, Odran, M., Dupont, Kochebina, Olga, Létang, Jean Michel, Rit, Simon, Abascal, Juan F P J, PEYRIN, Françoise, Morel, Christian, Boursier, Yannick, 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), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), European Synchrotron Radiation Facility (ESRF), 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
Spectral-CT, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, PIXSCAN-FLI, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, real data, Material decomposition
Abstract

International audience; The advent of hybrid pixels, a technological breakthrough that makes possible to capture spectral information in X-ray tomography, paves the way to material decomposition and quantification. In this paper, a two-material decomposition approach based on a calibration step is implemented. We present the first results obtained exploiting real data acquired by the PIXSCAN-FLI, a spectral micro-CT scanner that is equipped with an in-house photon-counting detector. Separation of Aluminium and PMMA in a small animal phantom is shown

Published
2017

8. Nonlinear material decomposition using a regularized iterative scheme based on the Bregman distance

Author

Abascal, Juan F P J, Ducros, Nicolas, Rit, Simon, PEYRIN, Françoise, 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), European Synchrotron Radiation Facility (ESRF), ANR-11-INBS-0006,FLI,France Life Imaging(2011), ANR-11-LABX-0063,PRIMES,Physique, Radiobiologie, Imagerie Médicale et Simulation(2011), European Project: 701915,H2020,H2020-MSCA-IF-2015,SUCCESS(2016), 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
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In this work, we address the solution of a nonlinear inverse problem. Due to the nonlinearity of the forward operator, the functional to minimise may be nonconvex and standard approaches may fail. We are motivated by the existence of iterative regularized schemes that replace the initial functional with a surrogate functional with improved convergence properties and that can lead to a global minimum. In particular, efficient iterative schemes based on the use of the Bregman distance have been proposed [M Bachmayr, M Burger, Inv Probl, 10 (2009), pp. 105004].The goal is to investigate the use of an iterative scheme based on the Bregman distance to solve the nonlinear material decomposition problem in spectral computerized tomography. Capitalising on a regularised Gauss Newton algorithm, the Bregman iterative scheme is created by replacing the regularization functional with its associated Bregman distance. First, we show that material decomposition is a nonconvex problem. Then, the proposed Bregman method is evaluated and compared to the Gauss Newton method. In particular, we focus on the convergence of the methods for different regularization parameters and initial guesses. Both methods are evaluated using data simulated in a realistic thorax phantom comprising three materials: soft tissue, bone and portal vein marked with gadolinium. Simulations include modelling of source spectrum and detector response functions, for four energy bins. Specific regularization is provided for each material: a smooth approximation of total variation for the marker and second order Tikhonov regularization for bone and soft tissue.The reference Gauss Newton approach required precise selection of the regularization parameter to provide an optimal solution and did not converge when the initial guess was far from the solution. On the contrary, the use of the Bregman distance led to similar solutions independently of the initial estimate and was more robust to the selection of the regularization parameter.

Published
2017

9. NONLINEAR MATERIAL DECOMPOSITION FOR X-RAY SPECTRAL IMAGING USING A BREGMAN ITERATIVE APPROACH

Author

Abascal, Juan F P J, Ducros, Nicolas, Rit, Simon, Sixou, Bruno, Peyrin, Françoise, 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), 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), 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), and European Synchrotron Radiation Facility (ESRF)

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; The new generation of photon counting Spectral Computed Tomography (SCT) scanners provide energy-dependent information that translates into material decomposition capabilities [1]. However, the increase in energy resolution leads to lower signal-tonoise ratio than standard CT scanners, so progresses in data processing are required. Recently, our group proposed to address the material decomposition problem in the projection domain as a non-linear inverse problem regularized by introducing some priors [2]. The use of the Bregman distance has shown to be very efficient to minimize convex functionals for linear measurements [3-5]. Its application to nonlinear measurements enjoys further benefits, such as improving global convergence properties, but it has not been widely explored [6]. In this work, we investigate an iterative regularized scheme based on the Bregman distance to solve the material decomposition problem in spectral CT for one projection image.1. K Taguchi et al, Med Phys, 40(10):100901, 20132. N Ducros et al, Proc. of CT Meeting, 20163. M Burger, Level Set and PDE Based Reconstruction Methods in Imaging, 1-70, 20134. T Goldstein and S Osher, SIAM J Imaging Sci, 2:323–343, 20095. JFPJ Abascal et al, Med Phys, 38(11):6275-84, 20116. M Bachmayr and M Burger, Inv Prob, 25:105004, 2009

Published
2017

17. Extended Joint Sparsity Reconstruction for Spatial and Temporal ERT Imaging.

Author

Chen, Bo, Abascal, Juan F. P. J., and Soleimani, Manuchehr

Subjects
*ELECTRODES, *IMAGE reconstruction, *ELECTRICAL resistance tomography, *INDUSTRIAL applications, *ELECTRIC conductivity
Abstract

Electrical resistance tomography (ERT) is an imaging technique to recover the conductivity distribution with boundary measurements via attached electrodes. There are a wide range of applications using ERT for image reconstruction or parameter calculation due to high speed data collection, low cost, and the advantages of being non-invasive and portable. Although ERT is considered a high temporal resolution method, a temporally regularized method can greatly enhance such a temporal resolution compared to frame-by-frame reconstruction. In some of the cases, especially in the industrial applications, dynamic movement of an object is critical. In practice, it is desirable for monitoring and controlling the dynamic process. ERT can determine the spatial conductivity distribution based on previous work, and ERT potentially shows good performance in exploiting temporal information as well. Many ERT algorithms reconstruct images frame by frame, which is not optimal and would assume that the target is static during collection of each data frame, which is inconsistent with the real case. Although spatiotemporal-based algorithms can account for the temporal effect of dynamic movement and can generate better results, there is not that much work aimed at analyzing the performance in the time domain. In this paper, we discuss the performance of a novel spatiotemporal total variation (STTV) algorithm in both the spatial and temporal domain, and Temporal One-Step Tikhonov-based algorithms were also employed for comparison. The experimental results show that the STTV has a faster response time for temporal variation of the moving object. This robust time response can contribute to a much better control process which is the main aim of the new generation of process tomography systems. [ABSTRACT FROM AUTHOR]

Published
2018
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18. Imagerie X spectrale: décomposition en base de matériaux par calibration polynomiale

Author

Nicolas Ducros, Odran Pivot, Simon Rit, Jean Michel Létang, Abascal, Juan F. P. J., Yannick Boursier, Mathieu Dupont, Christian Morel, Françoise Peyrin, 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), 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), 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), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), European Synchrotron Radiation Facility (ESRF), 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
[INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; La tomographie X spectrale est une modalité émergente qui suscite un intérêt croissant dans le domaine de l'imagerie médicale. Grâce à l’utilisation de nouveaux détecteurs à comptage de photons (DCP) capables de distinguer les photons X en fonction de leur énergie [1-2], il devient envisageable de reconstruire la concentration des constituants du corps humain comme l'os, l'eau, la graisse ou des agents de contraste tels que l’iode, le gadolinium ou l’or. Une approche possible consiste à décomposer les projections dans une base de matériaux, avant d’effectuer une reconstruction tomographique à partir des projections décomposées. Un des principaux avantages de cette approche est que l’étape de décomposition peut s’effectuer indépendamment – donc en parallèle– pour chaque angle de projection. Le problème de la décomposition en base de matériaux consiste à reconstruire, pour un angle de projection donné, la masse projetée des matériaux constitutifs de l’objet étudié à partir des nombres de photons mesurés par le DCP. Nous proposons de résoudre ce problème par une méthode de calibration dont l’un des mérites essentiels consiste à s’affranchir de la connaissance du spectre du tube et de la fonction de réponse en énergie du détecteur.

19. Electrical Resistance Tomography for Visualization of Moving Objects Using a Spatiotemporal Total Variation Regularization Algorithm.

Author

Chen, Bo, Abascal, Juan F. P. J., and Soleimani, Manuchehr

Subjects
*ELECTRICAL resistance tomography, *MATHEMATICAL regularization, *MANUFACTURING processes, *COMPUTER algorithms, *IMAGE reconstruction, *ACQUISITION of data
Abstract

Electrical resistance tomography (ERT) has been considered as a data collection and image reconstruction method in many multi-phase flow application areas due to its advantages of high speed, low cost and being non-invasive. In order to improve the quality of the reconstructed images, the Total Variation algorithm attracts abundant attention due to its ability to solve large piecewise and discontinuous conductivity distributions. In industrial processing tomography (IPT), techniques such as ERT have been used to extract important flow measurement information. For a moving object inside a pipe, a velocity profile can be calculated from the cross correlation between signals generated from ERT sensors. Many previous studies have used two sets of 2D ERT measurements based on pixel-pixel cross correlation, which requires two ERT systems. In this paper, a method for carrying out flow velocity measurement using a single ERT system is proposed. A novel spatiotemporal total variation regularization approach is utilised to exploit sparsity both in space and time in 4D, and a voxel-voxel cross correlation method is adopted for measurement of flow profile. Result shows that the velocity profile can be calculated with a single ERT system and that the volume fraction and movement can be monitored using the proposed method. Both semi-dynamic experimental and static simulation studies verify the suitability of the proposed method. For in plane velocity profile, a 3D image based on temporal 2D images produces velocity profile with accuracy of less than 1% error and a 4D image for 3D velocity profiling shows an error of 4%. [ABSTRACT FROM AUTHOR]

Published
2018
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