Zobrazeno 1 - 10
of 157
pro vyhledávání: '"Caroline Lafond"'
Autor:
Blanche Texier, Cédric Hémon, Adélie Queffélec, Jason Dowling, Igor Bessieres, Peter Greer, Oscar Acosta, Adrien Boue-Rafle, Renaud de Crevoisier, Caroline Lafond, Joël Castelli, Anaïs Barateau, Jean-Claude Nunes
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 31, Iss , Pp 100612- (2024)
Background and purpose: Magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis is essential in MRI-only radiotherapy workflows, particularly through deep learning techniques known for their accuracy. However, current supervised method
Externí odkaz:
https://doaj.org/article/2df01f73d36c41818e47ef3d0b41267e
Autor:
Safaa Tahri, Blanche Texier, Jean-Claude Nunes, Cédric Hemon, Pauline Lekieffre, Emma Collot, Hilda Chourak, Jennifer Le Guevelou, Peter Greer, Jason Dowling, Oscar Acosta, Igor Bessieres, Louis Marage, Adrien Boue-Rafle, Renaud De Crevoisier, Caroline Lafond, Anaïs Barateau
Publikováno v:
Frontiers in Oncology, Vol 13 (2023)
IntroductionFor radiotherapy based solely on magnetic resonance imaging (MRI), generating synthetic computed tomography scans (sCT) from MRI is essential for dose calculation. The use of deep learning (DL) methods to generate sCT from MRI has shown e
Externí odkaz:
https://doaj.org/article/a1d51ded1429495bb38f6ed943c64561
Autor:
Blanche Texier, Cédric Hémon, Pauline Lekieffre, Emma Collot, Safaa Tahri, Hilda Chourak, Jason Dowling, Peter Greer, Igor Bessieres, Oscar Acosta, Adrien Boue-Rafle, Jennifer Le Guevelou, Renaud de Crevoisier, Caroline Lafond, Joël Castelli, Anaïs Barateau, Jean-Claude Nunes
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 28, Iss , Pp 100511- (2023)
Background and Purpose: Addressing the need for accurate dose calculation in MRI-only radiotherapy, the generation of synthetic Computed Tomography (sCT) from MRI has emerged. Deep learning (DL) techniques, have shown promising results in achieving h
Externí odkaz:
https://doaj.org/article/93f14f1c64e24cf186ea07d7a70d3cb3
Autor:
Coralie Le Deroff, Lucie Berger, Julien Bellec, Guillaume Boissonnat, Héléna Chesneau, Sophie Chiavassa, Julie Desrousseaux, Stéphanie Gempp, Olivier Henry, Jimmy Jarril, Delphine Lazaro, Ronan Lefeuvre, Vincent Passal, Fanny Solinhac, Caroline Lafond, Gregory Delpon
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 21, Iss , Pp 108-114 (2022)
Background and purpose: Image-guided radiotherapy (IGRT) involves frequent in-room imaging sessions contributing to additional patient irradiation. The present work provided patient-specific dosimetric data related to different imaging protocols and
Externí odkaz:
https://doaj.org/article/55f8b85953884dadaa60017d9adcb6cf
Autor:
Hilda Chourak, Anaïs Barateau, Safaa Tahri, Capucine Cadin, Caroline Lafond, Jean-Claude Nunes, Adrien Boue-Rafle, Mathias Perazzi, Peter B. Greer, Jason Dowling, Renaud de Crevoisier, Oscar Acosta
Publikováno v:
Frontiers in Oncology, Vol 12 (2022)
The quality assurance of synthetic CT (sCT) is crucial for safe clinical transfer to an MRI-only radiotherapy planning workflow. The aim of this work is to propose a population-based process assessing local errors in the generation of sCTs and their
Externí odkaz:
https://doaj.org/article/2401c2d187f14405846db2e879e5f08d
Autor:
Caroline Lafond, Anaïs Barateau, Joël N'Guessan, Nicolas Perichon, Nolwenn Delaby, Antoine Simon, Pascal Haigron, Eugenia Mylona, Oscar Acosta, Renaud de Crevoisier
Publikováno v:
Frontiers in Oncology, Vol 10 (2020)
Background: A rectal sub-region (SRR) has been previously identified by voxel-wise analysis in the inferior-anterior part of the rectum as highly predictive of rectal bleeding (RB) in prostate cancer radiotherapy. Translating the SRR to patient-speci
Externí odkaz:
https://doaj.org/article/f2915440eba54ac2ad34f11698e546d9
Autor:
Chen Zhang, Caroline Lafond, Anaïs Barateau, Julie Leseur, Bastien Rigaud, Diane Barbara Chan Sock Line, Guanyu Yang, Huazhong Shu, Jean-Louis Dillenseger, Renaud de Crevoisier, Antoine Simon
Publikováno v:
Physics in Medicine and Biology
Physics in Medicine and Biology, 2022, 67 (24), pp.245020. ⟨10.1088/1361-6560/aca5e5⟩
Physics in Medicine and Biology, 2022, 67 (24), pp.245020. ⟨10.1088/1361-6560/aca5e5⟩
Objective. Plan-of-the-day (PoD) adaptive radiation therapy (ART) is based on a library of treatment plans, among which, at each treatment fraction, the PoD is selected using daily images. However, this strategy is limited by PoD selection uncertaint
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f72167c45c1d261629c959ce24f762e
https://hal.science/hal-03882318
https://hal.science/hal-03882318
Autor:
Hilda Chourak, Anaïs Barateau, Jean-Claude Nunes, Peter B. Greer, Safaa Tahri, Caroline Lafond, Renaud de Crevoisier, Jason Dowling, Oscar Acosta
Publikováno v:
2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA).
Autor:
Caroline Lafond, A. Barateau, Peter B. Greer, R. De Crevoiser, Jason Dowling, Oscar Acosta, C. Cadin, H. Chourak, Jean-Claude Nunes
Publikováno v:
Radiotherapy and Oncology. 161:S1408-S1410
Autor:
Eugenia Mylona, Peter B. Greer, Jason Dowling, Renaud de Crevoisier, Jean-Claude Nunes, Caroline Lafond, Hervé Saint-Jalmes, A. Barateau, John S. H. Baxter, Joël Castelli, A. Largent, Oscar Acosta
Publikováno v:
International Journal of Radiation Oncology, Biology, Physics
International Journal of Radiation Oncology, Biology, Physics, 2019, 105 (5), pp.1137-1150. ⟨10.1016/j.ijrobp.2019.08.049⟩
International Journal of Radiation Oncology-Biology-Physics
International Journal of Radiation Oncology-Biology-Physics, Elsevier, 2019, 105 (5), pp.1137-1150. ⟨10.1016/j.ijrobp.2019.08.049⟩
International Journal of Radiation Oncology, Biology, Physics, 2019, 105 (5), pp.1137-1150. ⟨10.1016/j.ijrobp.2019.08.049⟩
International Journal of Radiation Oncology-Biology-Physics
International Journal of Radiation Oncology-Biology-Physics, Elsevier, 2019, 105 (5), pp.1137-1150. ⟨10.1016/j.ijrobp.2019.08.049⟩
Purpose Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for magnetic resonance imaging (MRI) based dose planning. This study aims to evaluate and compare DLMs (U-Net and generative adversarial network [GAN]) using