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9 results on '"Delmon, V."'

1. Keuhkot: a method for lung segmentation

Author

Pinho, R., Delmon, V., Vandemeulebroucke, J., Simon Rit, Sarrut, D., 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), Department of Radiation Oncology, Centre Léon Bérard [Lyon], 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), Iminds [Ghent], Universiteit Gent = Ghent University [Belgium] (UGENT), Department of Electronics and Informatics [Vrije Univ Brussel] (ETRO), and Vrije Universiteit Brussel (VUB)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, categₛt2i, Imagerie tomographique et thérapie par rayonnement, reseauₙational, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2011

2. VV, a 4D slicer

Author

Rit, S., Pinho, R., Delmon, V., Pech, M., Bouilhol, G, Schaerer, J., Navalpakkam, B., Vandemeulebroucke, J., Seroul, P., Sarrut, D., 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), Department of Radiation Oncology, Centre Léon Bérard [Lyon], Iminds [Ghent], Universiteit Gent = Ghent University [Belgium] (UGENT), Department of Electronics and Informatics [Vrije Univ Brussel] (ETRO), and Vrije Universiteit Brussel (VUB)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, categₛt2i, Imagerie tomographique et thérapie par rayonnement, reseauₙational, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2011

3. In-room breathing motion estimation from limited projection views using a sliding deformation model

Author

UCL - SST/ICTM - Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Delmon, V., Vandemeulebroucke, J., Pinho, R., Vila Oliva, M., Sarrut, D., Rit, S., 17th International Conference on the Use of Computers in Radiation Therapy, ICCR 2013, UCL - SST/ICTM - Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Delmon, V., Vandemeulebroucke, J., Pinho, R., Vila Oliva, M., Sarrut, D., Rit, S., and 17th International Conference on the Use of Computers in Radiation Therapy, ICCR 2013

Abstract

Purpose: To estimate in-room breathing motion from a limited number of 2D cone-beam (CB) projection images by registering them to a phase of the 4D planning CT. Methods: Breathing motion was modelled using a piecewise continuous B-spline representation [1], allowing to preserve the sliding along the thoracic wall while limiting the degrees of freedom. The deformed target 3D image was subsequently used to generate Digitally Reconstructed Radiographs (DRR). The Normalized Correlation Coefficient (NCC) between the measured projection images and the DRR was computed in the 2D projection space. However, the partial derivatives of the NCC relative to the transform parameters were backprojected into the 3D space, avoiding the projection of the transform Jacobian matrix which is computationally intractable [2]. Results: The method was quantitatively evaluated on 16 lung cancer patients. 40 CB projection images were simulated using the end-exhale phase of the 4D planning CT and the geometric parameters of a clinical CB protocol. The end-inhale phase was deformed to match these simulated projections. The Target Registration Error (TRE) decreased from 8.8 mm to 2.0 mm while the TRE obtained from the 3D/3D registration of the reconstructed CBCT was significantly worse (2.6 mm), due to view aliasing artefacts. We also provide the motion compensated image reconstructed from a real CB acquisition showing the quality improvement brought by the in-room deformation model compared to the planning motion model. Conclusions: We have developed a 2D/3D deformable registration algorithm that enables in-room breathing motion estimation from cone-beam projection images.

Published
2014

8. Open source tools for validation of deformable registration

Author

Sarrut, D., Vandemeulebroucke, J., Bouilhol, G., Pinho, R., Delmon, V., Simon Rit, Centre Léon Bérard [Lyon], 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), Department of Electronics and Informatics [Vrije Univ Brussel] (ETRO), and Vrije Universiteit Brussel (VUB)

Subjects
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Deformable image registration is of considerable interest to the radiation therapy community. It is considered a key enabling technology for applications such as adaptive radiotherapy, contour propagation in 4D CT images, atlas-based segmentation, breathing motion modeling, dose accumulation and many more. Deformable registration is slowly finding its way to commercial software packages. However, validation of the estimated deformation fields remains challenging because no ground truth is available. In general, validation of deformable registration relies on manually generated data, often in the form of hand-clicked landmarks or handdrawn contours. Obtaining such data is a laborious and time-consuming task. Strictly speaking, the resulting validation should be considered as partial, due to the high number of degrees of freedom of the system. In addition, the ill-posed nature of the problem implies the procedure needs to be repeated when dealing with images of different quality, nature or modality. Open-source and open-data concepts constitute an approach where source code and data are provided for free usage, within the limits of a defined agreement, and with a strong concern on users' feedback and participation. It has proved to be an efficient strategy to accelerate scientific advances in a difficult field, based on distributed and collaborative effort, in particular by enabling evaluation and comparison. Many initiatives exist for the application of validating deformable registration, several of which have had a considerable impact in the field. In this presentation, we will describe some open-source and open-data tools that have been proposed and used for validation of deformable image registration. We will review some freely available image processing toolkits (e.g. ITK, elastix), publicly accessible databases of annotated images (e.g. Dir-Lab, POPI), visualization freeware (e.g. VTK, Osirix, VV) and implementation contests or challenges (EMPIRE10). Advantages and limitations of each will also be discussed.

9. 2D/4D marker-free tumor tracking using 4D CBCT as the reference image.

Author

Wang M, Sharp GC, Rit S, Delmon V, and Wang G

Subjects
Humans, Image Processing, Computer-Assisted, Neoplasms physiopathology, Respiration, Cone-Beam Computed Tomography methods, Fluoroscopy methods, Four-Dimensional Computed Tomography methods, Neoplasms diagnostic imaging
Abstract

Tumor motion caused by respiration is an important issue in image-guided radiotherapy. A 2D/4D matching method between 4D volumes derived from cone beam computed tomography (CBCT) and 2D fluoroscopic images was implemented to track the tumor motion without the use of implanted markers. In this method, firstly, 3DCBCT and phase-rebinned 4DCBCT are reconstructed from cone beam acquisition. Secondly, 4DCBCT volumes and a streak-free 3DCBCT volume are combined to improve the image quality of the digitally reconstructed radiographs (DRRs). Finally, the 2D/4D matching problem is converted into a 2D/2D matching between incoming projections and DRR images from each phase of the 4DCBCT. The diaphragm is used as a target surrogate for matching instead of using the tumor position directly. This relies on the assumption that if a patient has the same breathing phase and diaphragm position as the reference 4DCBCT, then the tumor position is the same. From the matching results, the phase information, diaphragm position and tumor position at the time of each incoming projection acquisition can be derived. The accuracy of this method was verified using 16 candidate datasets, representing lung and liver applications and one-minute and two-minute acquisitions. The criteria for the eligibility of datasets were described: 11 eligible datasets were selected to verify the accuracy of diaphragm tracking, and one eligible dataset was chosen to verify the accuracy of tumor tracking. The diaphragm matching accuracy was 1.88 ± 1.35 mm in the isocenter plane and the 2D tumor tracking accuracy was 2.13 ± 1.26 mm in the isocenter plane. These features make this method feasible for real-time marker-free tumor motion tracking purposes.

Published
2014
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