Proton dose calculation on cone-beam computed tomography using unsupervised 3D deep learning networks.

ABSTRACT:

Background and purpose

Poor image quality of cone-beam computed tomography (CBCT) images can hinder proton dose calculation to assess the influence of anatomy changes. The aim of this study was to evaluate image quality and proton dose calculation accuracy of synthetic CTs generated from CBCT using unsupervised 3D deep-learning networks.

Materials and methods

A total of 102 head-and-neck cancer patients were used to train (N=82) and test (N=20) i) a cycle-consistent generative adversarial network, ii) a contrastive unpaired translation, and iii) a fusion of the two (CycleCUT). For patients in the test set, a repeat CT was deformably registered to a same-day CBCT to create a ground-truth CT for comparison. The proton plan was re-calculated on the ground-truth CT and synthetic

SUBMITTER: Dueholm Vestergaard C 

PROVIDER: S-EPMC11554915 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature