A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer.

ABSTRACT: Background and purpose Adaptive radiotherapy based on cone-beam computed tomography (CBCT) requires high CT number accuracy to ensure accurate dose calculations. Recently, deep learning has been proposed for fast CBCT artefact corrections on single anatomical sites. This study investigated the feasibility of applying a single convolutional network to facilitate dose calculation based on CBCT for head-and-neck, lung and breast cancer patients. Materials and Methods Ninety-nine patients diagnosed with head-and-neck, lung or breast cancer undergoing radiotherapy with CBCT-based position verification were included in this study. The CBCTs were registered to planning CT according to clinical procedures. Three cycle-consistent generative adversarial networks (cycle-GANs) were train

SUBMITTER: Maspero M 

PROVIDER: S-EPMC7807541 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature