Cargando…
A feasibility study on an automated method to generate patient‐specific dose distributions for radiotherapy using deep learning
PURPOSE: To develop a method for predicting optimal dose distributions, given the planning image and segmented anatomy, by applying deep learning techniques to a database of previously optimized and approved Intensity‐modulated radiation therapy treatment plans. METHODS: Eighty cases of early‐stage...
Autores principales: | Chen, Xinyuan, Men, Kuo, Li, Yexiong, Yi, Junlin, Dai, Jianrong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7379709/ https://www.ncbi.nlm.nih.gov/pubmed/30367492 http://dx.doi.org/10.1002/mp.13262 |
Ejemplares similares
-
Automated treatment planning of postmastectomy radiotherapy
por: Kisling, Kelly, et al.
Publicado: (2019) -
Robust automated radiation therapy treatment planning using scenario‐specific dose prediction and robust dose mimicking
por: Eriksson, Oskar, et al.
Publicado: (2022) -
Fully automated treatment planning for MLC‐based robotic radiotherapy
por: Schipaanboord, Bastiaan W.K., et al.
Publicado: (2021) -
Neural network dose prediction for rectal spacer stratification in dose‐escalated prostate radiotherapy
por: Thomas, Christopher, et al.
Publicado: (2022) -
Clinical acceptability of fully automated external beam radiotherapy for cervical cancer with three different beam delivery techniques
por: Rhee, Dong Joo, et al.
Publicado: (2022)