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Embarrassingly Parallel Acceleration of Global Tractography via Dynamic Domain Partitioning
Haiyong Wu1, Geng Chen2, Yan Jin2
1School of Information Engineering, Xiaozhuang University, Nanjing, China; Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA.
Frontiers in Neuroinformatics
|July 29, 2016
Summary
This study introduces a faster global tractography method for brain connectivity analysis. The parallelized algorithm significantly reduces computation time, making it more suitable for clinical applications.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Medical Physics
Background:
- Global tractography estimates brain connectivity using diffusion-weighted data.
- Current global tractography is computationally intensive, limiting clinical use.
- Existing methods are less stable against imaging noise compared to global approaches.
Purpose of the Study:
- To reformulate the global tractography algorithm for faster parallel implementation.
- To enable acceleration using multi-core CPUs and general-purpose GPUs.
- To maintain or improve tractography performance while reducing computation time.
Main Methods:
- Developed a parallelized Markov chain Monte Carlo (MCMC) algorithm for global tractography.
- Leveraged the limited spatial neighborhood influence of fiber segments.
- Enabled concurrent updating of independent fiber segments.
Main Results:
- Achieved significant speed-up of global tractography computation.
- Maintained or improved tractography performance compared to existing methods.
- Demonstrated feasibility for acceleration on multi-core CPUs and GPUs.
Conclusions:
- The proposed parallel reformulation enhances the efficiency of global tractography.
- This advancement makes global tractography more viable for clinical applications.
- The method offers a computationally efficient and stable approach to brain connectivity estimation.
