  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.
