Accelerating Neuroimage Registration through Parallel Computation of Similarity Metric

PloS One
Yun-Gang LuoDefeng Wang

Abstract

Neuroimage registration is crucial for brain morphometric analysis and treatment efficacy evaluation. However, existing advanced registration algorithms such as FLIRT and ANTs are not efficient enough for clinical use. In this paper, a GPU implementation of FLIRT with the correlation ratio (CR) as the similarity metric and a GPU accelerated correlation coefficient (CC) calculation for the symmetric diffeomorphic registration of ANTs have been developed. The comparison with their corresponding original tools shows that our accelerated algorithms can greatly outperform the original algorithm in terms of computational efficiency. This paper demonstrates the great potential of applying these registration tools in clinical applications.

References

Aug 23, 2001·Medical Image Analysis·M Jenkinson, S Smith
Dec 17, 2009·Computer Methods and Programs in Biomedicine·Ramtin ShamsRichard Hartley
Nov 15, 2012·Genome Research·Jun WuShaoling Zhang
Aug 3, 2013·Medical Image Analysis·Anders EklundStephen M LaConte

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Citations

Sep 27, 2019·Medical Image Analysis·Jingfan FanDinggang Shen

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Software Mentioned

windows
SPM
DEMONS
FMRIB Software Library ( FSL )
ANTs
FNIRT FMRIB
NiftyReg
CUDA SDK
ITK
CUDA

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