Generation of Synthetic CT Images From MRI for Treatment Planning and Patient Positioning Using a 3-Channel U-Net Trained on Sagittal Images

A novel deep learning architecture was explored to create synthetic CT (MRCT) images that preserve soft tissue contrast necessary for support of patient positioning in Radiation therapy. A U-Net architecture was applied to learn the correspondence between input T1-weighted MRI and spatially aligned...

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Detalles Bibliográficos
Autores principales: Gupta, Dinank, Kim, Michelle, Vineberg, Karen A., Balter, James M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6773822/
https://www.ncbi.nlm.nih.gov/pubmed/31608241
http://dx.doi.org/10.3389/fonc.2019.00964

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6773822/
https://www.ncbi.nlm.nih.gov/pubmed/31608241
http://dx.doi.org/10.3389/fonc.2019.00964

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