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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2019
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| Materias: | |
| 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 |