A Contour-Guided Deformable Image Registration Algorithm for Adaptive Radiotherapy

TL;DR

This paper introduces a contour-guided deformable image registration algorithm that enhances the accuracy and consistency of deformation vector fields in adaptive radiotherapy by incorporating edited contours into the registration process.

Contribution

The proposed CG-DIR algorithm integrates edited contours into the registration process, improving DVF accuracy and consistency while maintaining computational efficiency.

Findings

CG-DIR improves DVF accuracy over traditional methods.

The algorithm maintains high computational efficiency.

Validated on clinical head-and-neck and pelvic cancer data.

Abstract

In adaptive radiotherapy, deformable image registration is often conducted between the planning CT and treatment CT (or cone beam CT) to generate a deformation vector field (DVF) for dose accumulation and contour propagation. The auto propagated contours on the treatment CT may contain relatively large errors, especially in low contrast regions. A clinician inspection and editing of the propagated contours are frequently needed. The edited contours are able to meet the clinical requirement for adaptive therapy; however, the DVF is still inaccurate and inconsistent with the edited contours. The purpose of this work is to develop a contour-guided deformable image registration (CG-DIR) algorithm to improve the accuracy and consistency of the DVF for adaptive radiotherapy. Incorporation of the edited contours into the registration algorithm is realized by regularizing the objective function of the original demons algorithm with a term of intensity matching between the delineated structures set pairs. The CG-DIR algorithm is implemented on computer graphics processing units (GPUs) by following the original GPU-based demons algorithm computation framework [Gu et al, Phys Med Biol. 55(1): 207-219, 2010]. The performance of CG-DIR is evaluated on five clinical head-and-neck and one pelvic cancer patient data. It is found that compared with the original demons, CG-DIR improves the accuracy and consistency of the DVF, while retaining similar high computational efficiency.