Frontiers in Neuroinformatics
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.
Original Publication Citation
Liu, Y. X., Kot, A., Drakopoulos, F., Yao, C. J., Fedorov, A., Enquobahrie, A., . . . Chrisochoides, N. P. (2014). An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery. Frontiers in Neuroinformatics, 8, 1-10. doi: 10.3389/fninf.2014.00033
Liu, Yixun; Kot, Andriy; Drakopoulos, Fotis; Yao, Chengjun; Fedorov, Andriy; Enquobahrie, Andinet; Clatz, Oliver; and Chrisochoides, Nikos P., "An ITK Implementation of a Physics-Based Non-Rigid Registration Method For Brain Deformation in Image Guided Neurosurgery" (2014). Electrical & Computer Engineering Faculty Publications. 70.