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Related Experiment Video

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Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery
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Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes.

Moises Hernandez-Fernandez1, Istvan Reguly2, Saad Jbabdi3

  • 1Wellcome Centre for Integrative Neuroimaging - Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom; Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States.

Neuroimage
|December 12, 2018
PubMed
Summary
This summary is machine-generated.

We developed two GPU frameworks to accelerate computational diffusion MRI (dMRI) analysis. These frameworks significantly speed up microstructure estimation and tractography, overcoming computational limitations for brain imaging research.

Keywords:
Bayesian inferenceBiophysical modellingBrain connectivityFibre dispersionFibre orientationsGPGPUMedical imagingNon-linear optimisationScientific computing

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biophysics

Background:

  • Computational diffusion MRI (dMRI) enables indirect inference of tissue microstructure and brain connectivity.
  • Current dMRI modeling and tractography are computationally intensive, limiting data exploration and methodology development.
  • Increasing dataset sizes and model complexity exacerbate computational demands.

Purpose of the Study:

  • To present two GPU-accelerated frameworks for dMRI computations.
  • To address the computational bottlenecks in biophysical modeling, microstructure estimation, and tractography.
  • To enable faster and more extensive analysis of dMRI data.

Main Methods:

  • Developed a GPU framework for biophysical modeling and microstructure estimation, featuring automated GPU code generation and accelerated Bayesian inference.
  • Developed a second GPU framework for probabilistic tractography and whole-brain connectome estimation, incorporating anatomical constraints like surface meshes.
  • Validated both frameworks against established CPU-based methods.

Main Results:

  • Achieved significant acceleration of dMRI computations using Graphics Processing Units (GPUs).
  • Demonstrated that a single GPU outperforms 200 CPU cores for typical dMRI applications.
  • Showcased the effectiveness of parallelized designs for both microstructure estimation and tractography.

Conclusions:

  • GPU acceleration offers a powerful solution to the computational challenges in dMRI.
  • The presented frameworks significantly enhance the efficiency of microstructure and connectivity analyses.
  • These advancements facilitate broader data exploration and methodological innovation in neuroimaging research.