Probabilistic brain fiber tractography on GPUs

Mo Xu, Xiaorui Zhang, Yu Wang, Ling Ren, Ziyu Wen, Yi Xu, Gaolang Gong, Ningyi Xu, Huazhong Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is an emerging technique that explores the structural connectivity of the human brain. The probabilistic fiber tractography based on DT-MRI data behaves more robustly than deterministic approaches in the presence of fiber crossings, but requires more prohibitive computational time. In this work we present a GPU-based probabilistic framework for brain fiber tractography. The framework includes two main steps: 1) Markov-Chain Monte-Carlo (MCMC) sampling, and 2) probabilistic streamlining fiber tracking. We implement the Metropolis-Hastings sampling for local parameter estimation on GPU. In the probabilistic streamlining fiber tracking, we find that fiber lengths are exponentially distributed, and propose a novel segmenting strategy to improve the load balance. On mid-range GPUs, we achieve performance gains up to 34x and 50x over CPUs for the two steps respectively.

Original languageEnglish (US)
Title of host publicationProceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012
Pages742-751
Number of pages10
DOIs
StatePublished - Oct 18 2012
Externally publishedYes
Event2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012 - Shanghai, China
Duration: May 21 2012May 25 2012

Publication series

NameProceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012

Other

Other2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012
CountryChina
CityShanghai
Period5/21/125/25/12

Keywords

  • DT-MRI
  • GPU
  • MCMC
  • Probabilistic Streamlining
  • Probabilistic fiber tractography

ASJC Scopus subject areas

  • Software

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