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An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery

 

Yixun Liu, Andriy Kot, Fotios Drakopoulos, Andriy Fedorov, Chengjun Yao, Andinet Enquobahrie, Olivier Clatz and Nikos Chrisochoides.

 

Published in Front. Neuroinform.,, Publisher Frontiers, doi: 10.3389/fninf.2014.00033, Volume 8, February, 2014

 

Abstract

 

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.