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An ITK Implementation of a Physics-based Non-rigid Registration Method for Brain Deformation in Image-guided Neurosurgery

1CRTC Lab and Computer Science, Old Dominion University Norfolk, VA, USA.
2Neurosurgery Department, Huashan Hospital Shanghai, China.
3Department of Radiology, Harvard Medical School, Brigham and Women's Hospital Boston, MA, USA.
4Kitware Inc. Clifton Park, NY, USA.
5Asclepios Research Laboratory, INRIA Sophia Antipolis Sophia Antipolis Cedex, France.
Frontiers Media SA
Publication Date:
Front Neuroinform
Volume Number:
Front Neuroinform. 2014 Apr 7;8:33.
PubMed ID:
GPU, ITK, block matching, Finite Element, image-guided neurosurgery, non-rigid registration
Appears in Collections:
P41 EB015898/EB/NIBIB NIH HHS/United States
Generated Citation:
Liu Y., Kot A., Drakopoulos F., Yao C., Fedorov A., Enquobahrie A., Clatz O., Chrisochoides N.P. An ITK Implementation of a Physics-based Non-rigid Registration Method for Brain Deformation in Image-guided Neurosurgery. Front Neuroinform. 2014 Apr 7;8:33. PMID: 24778613. PMCID: PMC3985035.
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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.

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