### 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 language | English |
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Title of host publication | Mathematics and Visualization |

Publisher | Springer Heidelberg |

Pages | 121-130 |

Number of pages | 10 |

Volume | none |

ISBN (Print) | 9783319285863 |

DOIs | |

Publication status | Published - 2016 |

Externally published | Yes |

Event | Workshop on Computational Diffusion MRI, MICCAI 2015 - Munich, Germany Duration: 2015 Oct 9 → 2015 Oct 9 |

### Publication series

Name | Mathematics and Visualization |
---|---|

Volume | none |

ISSN (Print) | 16123786 |

ISSN (Electronic) | 2197666X |

### Other

Other | Workshop on Computational Diffusion MRI, MICCAI 2015 |
---|---|

Country | Germany |

City | Munich |

Period | 15/10/9 → 15/10/9 |

### Fingerprint

### ASJC Scopus subject areas

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

### Cite this

*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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*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

}

TY - GEN

T1 - Accelerating global tractography using parallel Markov chain Monte Carlo

AU - Wu, Haiyong

AU - Chen, Geng

AU - Yang, Zhongxue

AU - Shen, Dinggang

AU - Yap, Pew Thian

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84964066800&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84964066800&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-28588-7_11

DO - 10.1007/978-3-319-28588-7_11

M3 - Conference contribution

AN - SCOPUS:84964066800

SN - 9783319285863

VL - none

T3 - Mathematics and Visualization

SP - 121

EP - 130

BT - Mathematics and Visualization

PB - Springer Heidelberg

ER -