Work

Embarrassingly Parallel Acceleration of Global Tractography via Dynamic Domain Partitioning

Year: 2016

Type: article

Abstract: Global tractography estimates brain connectivity by organizing signal-generating fiber segments in an optimal configuration 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 multi-core CPUs and general-purpose GPUs. Our method is motivated by the key observation that each fiber segment is affected by a limited spatial neighborhood. In other words, a fiber segment is influenced only by the fiber segments that are (or can potentially be) connected to its two 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 concurrent updating of independent fiber segments can be carried out. Experiments show that the proposed algorithm can significantly speed up global tractography, while at the same time maintain or even improve tractography performance.

Source: Frontiers in Neuroinformatics

DOI: 10.3389/fninf.2016.00025

Authors: Haiyong Wu, Geng Chen, Yan Jin, Dinggang Shen, Pew-Thian Yap

Institutions: University of North Carolina at Chapel Hill, Nanjing Xiaozhuang University, Korea University

Language: English

Topic: Advanced Neuroimaging Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: gold

Estimated APC paid: $3,801