Divide et impera: Acceleration of DTI tractography using multi-GPU parallel processing

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dc.contributor.authorLee, Jungsooko
dc.contributor.authorKim, Dae-Shikko
dc.date.accessioned2016-07-05T08:24:42Z-
dc.date.available2016-07-05T08:24:42Z-
dc.date.created2013-09-12-
dc.date.created2013-09-12-
dc.date.issued2013-09-
dc.identifier.citationINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, v.23, no.3, pp.256 - 264-
dc.identifier.issn0899-9457-
dc.identifier.urihttp://hdl.handle.net/10203/209382-
dc.description.abstractDiffusion tensor imaging (DTI) tractography technique represents neural fiber pathways by using local tensor information based on water diffusion anisotropy in brain white matter. However, DTI tractography is often unable to reconstruct crossing, kissing, and branching fiber trajectories due to intrinsic limitations of DTI. Increasingly complex tractography algorithms provided reliable and visually pleasing results, yet at an increasing computational cost in comparison with simple tractography algorithms. To shorten the computation time, we developed multi-GPU (graphics processing unit)-based parallelized versions of deterministic and probabilistic tractography algorithms to investigate their utility for near-real time tractography. We were able to dramatically reduce the computation time using multiple GPUs (three NVIDIA TESLA C1060s) in comparison to the central processing unit (CPU) sequential processing. Deterministic tractography could accelerate 101 times faster, and probabilistic tractography could accelerate 63 times faster. The results showed that parallel tractography algorithm is well suited with GPU which has fundamentally parallelized architecture. (c) 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 256-264, 2013-
dc.languageEnglish-
dc.publisherWILEY-BLACKWELL-
dc.subjectDIFFUSION-
dc.subjectBRAIN-
dc.subjectTRACKING-
dc.subjectCONNECTIONS-
dc.titleDivide et impera: Acceleration of DTI tractography using multi-GPU parallel processing-
dc.typeArticle-
dc.identifier.wosid000323169200007-
dc.identifier.scopusid2-s2.0-84882334103-
dc.type.rimsART-
dc.citation.volume23-
dc.citation.issue3-
dc.citation.beginningpage256-
dc.citation.endingpage264-
dc.citation.publicationnameINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY-
dc.identifier.doi10.1002/ima.22059-
dc.contributor.localauthorKim, Dae-Shik-
dc.contributor.nonIdAuthorLee, Jungsoo-
dc.type.journalArticleArticle-
dc.subject.keywordAuthortractography-
dc.subject.keywordAuthorGPU parallel processing-
dc.subject.keywordPlusDIFFUSION-
dc.subject.keywordPlusBRAIN-
dc.subject.keywordPlusTRACKING-
dc.subject.keywordPlusCONNECTIONS-
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