"Fast optimization methods for image registration in adaptive radiation therapy"
Image registration is important for medical image analysis. However, its clinical application is sometimes limited by the speed of the algorithm. For example, in online adaptive radiation therapy a few seconds is ideal, while it usually takes several minutes, at the least. In this thesis, we consider acceleration techniques for parametric intensity-based image registration problems focussing on the optimization routine, specifically the step size and the search direction. The different proposed methods are thoroughly evaluated on different datasets across modalities, subject, similarity measures and transformation models. Depending on the registration settings, the estimation time of the step size is reduced from 40 seconds to less than 1 second when the number of parameters is 105, almost 40 times faster. The total registration time of new acceleration techniques (FASGD) is reduced by a factor of 2.5-7x compared with ASGD for the experiments in this thesis. All methods were implemented using C++ in the open source registration package elastix. Based on these acceleration schemes we evaluated elastix on the application of automatic contour propagation in online adaptive intensity modulated proton therapy for prostate cancer.