A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging

PURPOSE/OBJECTIVES: An artificial intelligence-based pseudo-CT from low-field MR images is proposed and clinically evaluated to unlock the full potential of MRI-guided adaptive radiotherapy for pelvic cancer care. MATERIALS AND METHOD: In collaboration with TheraPanacea (TheraPanacea, Paris, France)...

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Detalles Bibliográficos
Autores principales: Prunaretty, Jessica, Güngör, Gorkem, Gevaert, Thierry, Azria, David, Valdenaire, Simon, Balermpas, Panagiotis, Boldrini, Luca, Chuong, Michael David, De Ridder, Mark, Hardy, Leo, Kandiban, Sanmady, Maingon, Philippe, Mittauer, Kathryn Elizabeth, Ozyar, Enis, Roque, Thais, Colombo, Lorenzo, Paragios, Nikos, Pennell, Ryan, Placidi, Lorenzo, Shreshtha, Kumar, Speiser, M. P., Tanadini-Lang, Stephanie, Valentini, Vincenzo, Fenoglietto, Pascal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667706/
https://www.ncbi.nlm.nih.gov/pubmed/38023165
http://dx.doi.org/10.3389/fonc.2023.1245054

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667706/
https://www.ncbi.nlm.nih.gov/pubmed/38023165
http://dx.doi.org/10.3389/fonc.2023.1245054

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