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fMRI Analysis on th...
fMRI Analysis on the GPU - Possibilities and Challenges
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- Eklund, Anders (författare)
- Linköpings universitet,Medicinsk informatik,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Tekniska högskolan
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- Andersson, Mats (författare)
- Linköpings universitet,Medicinsk informatik,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Tekniska högskolan
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- Knutsson, Hans (författare)
- Linköpings universitet,Medicinsk informatik,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Tekniska högskolan
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(creator_code:org_t)
- Elsevier, 2012
- 2012
- Engelska.
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Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier. - 0169-2607 .- 1872-7565. ; 105:2, s. 145-161
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Abstract
Ämnesord
Stäng
- Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activity with high spatial resolution.There are however a number of issues that have to be addressed. One is the large amount of spatio-temporal data that needsto be processed. In addition to the statistical analysis itself, several preprocessing steps, such as slice timing correction and motioncompensation, are normally applied. The high computational power of modern graphic cards has already successfully been used forMRI and fMRI. Going beyond the first published demonstration of GPU-based analysis of fMRI data, all the preprocessing stepsand two statistical approaches, the general linear model (GLM) and canonical correlation analysis (CCA), have been implementedon a GPU. For an fMRI dataset of typical size (80 volumes with 64 x 64 x 22 voxels), all the preprocessing takes about 0.5 s on theGPU, compared to 5 s with an optimized CPU implementation and 120 s with the commonly used statistical parametric mapping(SPM) software. A random permutation test with 10 000 permutations, with smoothing in each permutation, takes about 50 s ifthree GPUs are used, compared to 0.5 - 2.5 h with an optimized CPU implementation. The presented work will save time forresearchers and clinicians in their daily work and enables the use of more advanced analysis, such as non-parametric statistics, bothfor conventional fMRI and for real-time fMRI.
Nyckelord
- Functional magnetic resonance imaging (fMRI)
- Graphics processing unit (GPU)
- CUDA
- General linear model (GLM)
- Canonical correlation analysis (CCA)
- Random permutation test
- TECHNOLOGY
- TEKNIKVETENSKAP
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- art (ämneskategori)
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