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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Computational Neuroscience

Background:

  • Diffusion MRI (dMRI) data processing requires correcting for artifacts like eddy currents, motion distortion, and gradient inhomogeneities.
  • These corrections are computationally intensive, posing a significant bottleneck in dMRI analysis.
  • The integration of Compute Unified Device Architecture (CUDA) into the 'eddy' software offers a potential solution for accelerating these processes.

Purpose of the Study:

  • To evaluate the processing speed, performance, and compatibility of CUDA-enabled eddy-current correction compared to traditional non-CUDA methods.
  • To assess the impact of CUDA on the widely-used 'eddy' software for dMRI data correction.

Main Methods:

  • Four diverse dMRI datasets were processed using 'eddy' on both high-specification and regular workstations.
  • Three different configurations of 'eddy' were tested, with processing times and GPU resource utilization meticulously monitored.
  • CUDA-enabled and non-CUDA implementations were directly compared.

Main Results:

  • CUDA implementation reduced 'eddy' processing time by a factor of up to five.
  • The CUDA slice-to-volume correction method demonstrated superior speed compared to non-CUDA eddy, particularly for smaller datasets.
  • Specific hardware recommendations (32GB RAM, GPU with 4.5GB RAM and 3750 cores) were identified for optimal performance.

Conclusions:

  • Utilizing CUDA with 'eddy' software significantly accelerates dMRI processing, making it more computationally feasible.
  • The slice-to-volume correction option within CUDA-enabled 'eddy' is recommended for enhanced processing speed.
  • Adherence to recommended hardware specifications can further optimize processing times for dMRI analysis.