Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Boue-Rafle A"'
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:
Texier, Blanche, Hémon, Cédric, Queffélec, Adélie, Dowling, Jason, Bessieres, Igor, Greer, Peter, Acosta, Oscar, Boue-Rafle, Adrien, de Crevoisier, Renaud, Lafond, Caroline, Castelli, Joël, Barateau, Anaïs, Nunes, Jean-Claude
Publikováno v:
In Physics and Imaging in Radiation Oncology July 2024 31
Autor:
Boué-Raflé, A., Briens, A., Supiot, S., Blanchard, P., Baty, M., Lafond, C., Masson, I., Créhange, G., Cosset, J.-M., Pasquier, D., de Crevoisier, R.
Publikováno v:
In Cancer / Radiothérapie June 2024 28(3):293-307
Autor:
Texier, Blanche, Hémon, Cédric, Lekieffre, Pauline, Collot, Emma, Tahri, Safaa, Chourak, Hilda, Dowling, Jason, Greer, Peter, Bessieres, Igor, Acosta, Oscar, Boue-Rafle, Adrien, Guevelou, Jennifer Le, de Crevoisier, Renaud, Lafond, Caroline, Castelli, Joël, Barateau, Anaïs, Nunes, Jean-Claude
Publikováno v:
In Physics and Imaging in Radiation Oncology October 2023 28
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:
Cubero, Lucía, García-Elcano, Laura, Mylona, Eugenia, Boue-Rafle, Adrien, Cozzarini, Cesare, Ubeira Gabellini, Maria Giulia, Rancati, Tiziana, Fiorino, Claudio, de Crevoisier, Renaud, Acosta, Oscar, Pascau, Javier
Publikováno v:
In Physics and Imaging in Radiation Oncology April 2023 26
Autor:
Lucía Cubero, Laura García-Elcano, Eugenia Mylona, Adrien Boue-Rafle, Cesare Cozzarini, Maria Giulia Ubeira Gabellini, Tiziana Rancati, Claudio Fiorino, Renaud de Crevoisier, Oscar Acosta, Javier Pascau
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 26, Iss , Pp 100431- (2023)
Background and purpose: The intraprostatic urethra is an organ at risk in prostate cancer radiotherapy, but its segmentation in computed tomography (CT) is challenging. This work sought to: i) propose an automatic pipeline for intraprostatic urethra
Externí odkaz:
https://doaj.org/article/ea6453324c014999ac8038d4d81fc3eb
Akademický článek
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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