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 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.
CRTC Lab and Computer Science, Old Dominion University Norfolk, VA, USA ; Radiology and Imaging Sciences, National Institutes of Health Bethesda, MD, USA.
Yixun
Y
Liu
CRTC Lab and Computer Science, Old Dominion University Norfolk, VA, USA.
Andriy
A
Kot
CRTC Lab and Computer Science, Old Dominion University Norfolk, VA, USA.
Fotis
F
Drakopoulos
Neurosurgery Department, Huashan Hospital Shanghai, China.
Chengjun
C
Yao
CRTC Lab and Computer Science, Old Dominion University Norfolk, VA, USA ; Radiology, Harvard Medical School, Brigham and Women's Hospital Boston, MA, USA.
Andriy
A
Fedorov
Kitware Inc. Clifton Park, NY, USA.
Andinet
A
Enquobahrie
Asclepios Research Laboratory, INRIA Sophia Antipolis Sophia Antipolis Cedex, France.
Olivier
O
Clatz
CRTC Lab and Computer Science, Old Dominion University Norfolk, VA, USA.
Nikos P
NP
Chrisochoides
GPU; ITK; block matching; finite element; image-guided neurosurgery; non-rigid registration