Search

Purpose: Accurate delineation of the rectum is of high importance in off-line adaptive radiation therapy since it is a major dose-limiting organ in prostate cancer radiotherapy. The intensity-based deformable image registration (DIR) methods cannot create a correct spatial transformation if there is no correspondence between the template and the target images. The variation of rectal filling, gas, or feces, creates a noncorrespondence in image intensities that becomes a great obstacle for intensity-based DIR. Methods: In this study the authors have designed and implemented a semiautomatic method to create a rectum mask in pelvic computed tomography (CT) images. The method, that includes a DIR based on the demons algorithm, has been tested in 13 prostate cancer cases, each comprising of two CT scans, for a total of 26 CT scans. Results: The use of the manual segmentation in the planning image and the proposed rectum mask method (RMM) method in the daily image leads to an improvement in the DIR performance in pelvic CT images, obtaining a mean value of overlap volume index = 0.89, close to the values obtained using the manual segmentations in both images. Conclusions: The application of the RMM method in the daily image and the manual segmentations in the planning image during prostate cancer treatments increases the performance of the registration in presence of rectal fillings, obtaining very good agreement with a physicianˈs manual contours.

Purpose: Current radiotherapy is progressing to the concept of adaptive radiotherapy, which implies the adaptation of planning along the treatment course. Nonrigid registration is an essential image processing tool for adaptive radiotherapy and image guided radiotherapy, and the three-dimensional (3D) nature of the current radiotherapy techniques requires a 3D quantification of the registration error that existing evaluation methods do not cover appropriately. The authors present a method for 3D evaluation of nonrigid registration algorithms’ performance, based on organ delineations, capable of working with near-spherical volumes even in the presence of concavities. Methods: The evaluation method is composed by a volume shape description stage, developed using a newad hoc volume reconstruction algorithm proposed by the authors, and an error quantification stage. The evaluation method is applied to the organ delineations of prostate and seminal vesicles, obtained by an automatic segmentation method over images of prostate cancer patients treated with intensity modulated radiation therapy. Results: The volume reconstruction algorithm proposed has been shown to accurately model complex 3D surfaces by the definition of clusters of control points. The quantification method, inspired by the Haussdorf–Chebysev distance, provides a measure of the largest registration error per control direction, defining a valid metric for concave-convex volumes. Summarizing, the proposed evaluation methodology presents accurate results with a high spatial resolution in a negligible computation time in comparison with the nonrigid registration time. Conclusions: Experimental results show that the metric selected for quantifying the registration error is of utmost importance in a quantitative evaluation based on measuring distances between volumes. The accuracy of the volume reconstruction algorithm is not so relevant as long as the reconstruction is tight enough on the actual volume of the organ. The new evaluation method provides a smooth and accurate volume reconstruction for both the reference and the registered organ, and a complete 3D description of nonrigid registration algorithms’ performance, resulting in a useful tool for study and comparison of registration algorithms for adaptive radiotherapy.