An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery
Chrisochoides, Nikos P.
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CitationLiu, Yixun, Andriy Kot, Fotis Drakopoulos, Chengjun Yao, Andriy Fedorov, Andinet Enquobahrie, Olivier Clatz, and Nikos P. Chrisochoides. 2014. “An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery.” Frontiers in Neuroinformatics 8 (1): 33. doi:10.3389/fninf.2014.00033. http://dx.doi.org/10.3389/fninf.2014.00033.
AbstractAs 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12152943
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