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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.
Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART is accurately and efficiently delineating organs at risk (OARs) and targets on online images, such
The purpose of this study is to develop a deep learning based method that can automatically generate segmentations on cone-beam CT (CBCT) for head and neck online adaptive radiation therapy (ART), where expert-drawn contours in planning CT (pCT) can
Purpose: Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided
Deformable image registration (DIR) is essential for many image-guided therapies. Recently, deep learning approaches have gained substantial popularity and success in DIR. Most deep learning approaches use the so-called mono-stream high-to-low, low-t
Deformable image registration, aiming to find spatial correspondence between a given image pair, is one of the most critical problems in the domain of medical image analysis. In this paper, we present a generic, fast, and accurate diffeomorphic image