An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery - Liu, Yixun; Kot, Andriy; Drakopoulos, Fotis; Yao, Chengjun; Fedorov, Andrey; Enquobahrie, Andinet; Clatz, Olivier; Chrisochoides, Nikos P. (Frontiers in Neuroinformatics, 2014)
As part of the ITK v4 project efforts, we have developed ITK filters for physics-based non-rigid registration (PBNRR), which satisfies the following requirements: account for tissue properties in the registration, improve accuracy compared to rigid registration, and reduce execution time using GPU and multi-core accelerators. The implementation has three main components: (1) Feature Point Selection, (2) Block Matching (mapped to both multi-core and GPU processors), and (3) a Robust Finite Element Solver. The use of multi-core and GPU accelerators in ITK v4 provides substantial performance improvements. For example, for the non-rigid registration of brain MRIs, the performance of the block matching filter on average is about 10 times faster when 12 hyperthreaded multi-cores are used and about 83 times faster when the NVIDIA Tesla GPU is used in Dell Workstation.