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

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

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 a...

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Bibliographic Details
Main Authors: Yixun eLiu, Andriy eKot, Fotis eDrakopoulos, Chengjun eYao, Andrey eFedorov, Andinet eEnquobahrie, Olivier eClatz, Nikos P Chrisochoides
Format: Article
Language:English
Published: Frontiers Media S.A. 2014-04-01
Series:Frontiers in Neuroinformatics
Subjects:
gpu
biomechanical model
ITK
non-rigid registration
tumor resection
image-guided neurosurgery
Online Access:http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00033/full
Description
Summary: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.
ISSN:1662-5196

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