Name
Title | ||
|---|---|---|
An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery. |
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3389/fninf.2014.00033 | FRONTIERS IN NEUROINFORMATICS |
Keywords | Field | DocType |
image-guided neurosurgery,non-rigid registration,block matching,finite element,ITK,GPU | Data mining,Computer science,Simulation,Workstation,Finite element method,Computational science,Execution time,Biomechanical model,Finite element solver | Journal |
Volume | ISSN | Citations |
8 | 1662-5196 | 4 |
PageRank | References | Authors |
0.47 | 9 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yixun Liu | 1 | 158 | 13.84 |
| Andriy Kot | 2 | 59 | 4.89 |
| Fotis Drakopoulos | 3 | 10 | 1.97 |
| Chengjun Yao | 4 | 4 | 0.47 |
| Andriy Fedorov | 5 | 171 | 16.54 |
| Andinet Enquobahrie | 6 | 73 | 15.45 |
| Olivier Clatz | 7 | 443 | 32.41 |
| Nikos Chrisochoides | 8 | 672 | 59.09 |