Accelerating global tractography using parallel Markov chain Monte Carlo

Haiyong Wu, Geng Chen, Zhongxue Yang, Dinggang Shen, Pew Thian Yap

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

Abstract

Global tractography estimates brain connectivity by determining the optimal configuration of signal-generating fiber segments that best describes the measured diffusion-weighted data, promising better stability than local greedy methods with respect to imaging noise. However, global tractography is computationally very demanding and requires computation times that are often prohibitive for clinical applications. We present here a reformulation of the global tractography algorithm for fast parallel implementation amendable to acceleration using multicore CPUs and general-purpose GPUs. Our method is motivated by the key observation that each fiber segment is affected by a limited spatial neighborhood. That is, a fiber segment is influenced only by the fiber segments that are (or can potentially be) connected to its both ends and also by the diffusion-weighted signal in its proximity. This observation makes it possible to parallelize the Markov chain Monte Carlo (MCMC) algorithm used in the global tractography algorithm so that updating of independent fiber segments can be done concurrently. The experiments show that the proposed algorithm can significantly speed up global tractography, while at the same time maintain or improve tractography performance.

Original languageEnglish
Title of host publicationMathematics and Visualization
PublisherSpringer Heidelberg
Pages121-130
Number of pages10
Volumenone
ISBN (Print)9783319285863
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventWorkshop on Computational Diffusion MRI, MICCAI 2015 - Munich, Germany
Duration: 2015 Oct 92015 Oct 9

Publication series

NameMathematics and Visualization
Volumenone
ISSN (Print)16123786
ISSN (Electronic)2197666X

Other

OtherWorkshop on Computational Diffusion MRI, MICCAI 2015
CountryGermany
CityMunich
Period15/10/915/10/9

Fingerprint

Markov Chain Monte Carlo
Markov processes
Fiber
Fibers
Markov Chain Monte Carlo Algorithms
Parallel Implementation
Reformulation
Proximity
Program processors
Updating
Brain
Connectivity
Speedup
Imaging
Imaging techniques
Configuration
Estimate
Experiment
Experiments
Observation

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics
  • Geometry and Topology
  • Modelling and Simulation

Cite this

Wu, H., Chen, G., Yang, Z., Shen, D., & Yap, P. T. (2016). Accelerating global tractography using parallel Markov chain Monte Carlo. In Mathematics and Visualization (Vol. none, pp. 121-130). (Mathematics and Visualization; Vol. none). Springer Heidelberg. https://doi.org/10.1007/978-3-319-28588-7_11

Accelerating global tractography using parallel Markov chain Monte Carlo. / Wu, Haiyong; Chen, Geng; Yang, Zhongxue; Shen, Dinggang; Yap, Pew Thian.

Mathematics and Visualization. Vol. none Springer Heidelberg, 2016. p. 121-130 (Mathematics and Visualization; Vol. none).

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

Wu, H, Chen, G, Yang, Z, Shen, D & Yap, PT 2016, Accelerating global tractography using parallel Markov chain Monte Carlo. in Mathematics and Visualization. vol. none, Mathematics and Visualization, vol. none, Springer Heidelberg, pp. 121-130, Workshop on Computational Diffusion MRI, MICCAI 2015, Munich, Germany, 15/10/9. https://doi.org/10.1007/978-3-319-28588-7_11
Wu H, Chen G, Yang Z, Shen D, Yap PT. Accelerating global tractography using parallel Markov chain Monte Carlo. In Mathematics and Visualization. Vol. none. Springer Heidelberg. 2016. p. 121-130. (Mathematics and Visualization). https://doi.org/10.1007/978-3-319-28588-7_11
Wu, Haiyong ; Chen, Geng ; Yang, Zhongxue ; Shen, Dinggang ; Yap, Pew Thian. / Accelerating global tractography using parallel Markov chain Monte Carlo. Mathematics and Visualization. Vol. none Springer Heidelberg, 2016. pp. 121-130 (Mathematics and Visualization).
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