Human-level comparable control volume mapping with a deep unsupervised-learning model for image-guided radiation therapy.

ABSTRACT:

Purpose

To develop a deep unsupervised learning method with control volume (CV) mapping from patient positioning daily CT (dCT) to planning computed tomography (pCT) for precise patient positioning.

Methods

We propose an unsupervised learning framework, which maps CVs from dCT to pCT to automatically generate the couch shifts, including translation and rotation dimensions. The network inputs are dCT, pCT and CV positions in the pCT. The output is the transformation parameter of the dCT used to setup the head and neck cancer (HNC) patients. The network is trained to maximize image similarity between the CV in the pCT and the CV in the dCT. A total of 554 CT scans from 158 HNC patients were used for the evaluation of the proposed model. At different points in time, each patien

SUBMITTER: Liang X 

PROVIDER: S-EPMC8810749 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature