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Updated: Apr 30, 2026
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided
Yixun Liu1, Andriy Kot2, Fotis Drakopoulos2
1CRTC Lab and Computer Science, Old Dominion University Norfolk, VA, USA ; Radiology and Imaging Sciences, National Institutes of Health Bethesda, MD, USA.
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.
