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Fiber Clustering Acceleration With a Modified Kmeans++ Algorithm Using Data Parallelism.

Isaac Goicovich1, Paulo Olivares2, Claudio Román1

  • 1Department of Electrical Engineering, Universidad de Concepción, Concepción, Chile.

Frontiers in Neuroinformatics
|September 20, 2021
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Summary

This study introduces a faster fiber clustering algorithm for brain white matter analysis using graphics processing units (GPUs). The new method significantly speeds up diffusion MRI tractography processing while maintaining high-quality results for neuroscience research.

Keywords:
GPGPU—CUDAdata parallelismfiber clusteringparallel computingwhite matter bundle

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Fiber clustering is crucial for analyzing white matter organization in diffusion MRI tractography.
  • Existing methods require efficient intra-subject clustering for applications like real-time visualization and inter-subject analysis.
  • Fast and high-quality clustering algorithms are needed for large-scale brain research.

Purpose of the Study:

  • To develop a parallel algorithm for fiber clustering using a General Purpose Graphics Processing Unit (GPGPU).
  • To accelerate the FFClust algorithm for improved performance in analyzing diffusion MRI tractography data.
  • To enhance the speed and efficiency of white matter bundle identification in neuroscience.

Main Methods:

  • Implementation of a parallel FFClust algorithm on GPGPU architectures.
  • Exploitation of multicore and GPU fine-grained parallelism for data processing.
  • Inclusion of a parallel Kmeans++ algorithm with a novel variant to mitigate outlier impact on centroid selection.

Main Results:

  • The proposed GPGPU approach achieves clustering quality comparable to the original FFClust algorithm.
  • Processing approximately one million fibers takes only 3.5 seconds.
  • A significant speedup of 11.5 times was observed compared to the non-parallel FFClust implementation.

Conclusions:

  • The parallel GPGPU algorithm offers a substantial performance improvement for fiber clustering in diffusion MRI tractography.
  • This method enables faster and more efficient analysis of white matter structures.
  • The approach is suitable for commodity hardware, facilitating broader application in brain research.