Deformable CT image registration via a dual feasible neural network.

ABSTRACT:

Purpose

A quality assurance (QA) CT scans are usually acquired during cancer radiotherapy to assess for any anatomical changes, which may cause an unacceptable dose deviation and therefore warrant a replan. Accurate and rapid deformable image registration (DIR) is needed to support contour propagation from the planning CT (pCT) to the QA CT to facilitate dose volume histogram (DVH) review. Further, the generated deformation maps are used to track the anatomical variations throughout the treatment course and calculate the corresponding accumulated dose from one or more treatment plans.

Methods

In this study, we aim to develop a deep learning (DL)-based method for automatic deformable registration to align the pCT and the QA CT. Our proposed method, named dual-feasible framewo

SUBMITTER: Lei Y 

PROVIDER: S-EPMC9792435 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature