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 |
|---|---|
| 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
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 proceeding › Conference contribution
}
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.
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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 -