Fast computation of myelin maps from MRI T₂ relaxation data using multicore CPU and graphics card parallelization.

Purpose: To develop a fast algorithm for computing myelin maps from multiecho T2 relaxation data using parallel computation with multicore CPUs and graphics processing units (GPUs).

Materials And Methods: Using an existing MATLAB (MathWorks, Natick, MA) implementation with basic (nonalgorithm-specific) parallelism as a guide, we developed a new version to perform the same computations but using C++ to optimize the hybrid utilization of multicore CPUs and GPUs, based on experimentation to determine which algorithmic components would benefit from CPU versus GPU parallelization. Using 32-echo T2 data of dimensions 256 × 256 × 7 from 17 multiple sclerosis patients and 18 healthy subjects, we compared the two methods in terms of speed, myelin values, and the ability to distinguish between the two patient groups using Student's t-tests.

Results: The new method was faster than the MATLAB implementation by 4.13 times for computing a single map and 14.36 times for batch-processing 10 scans. The two methods produced very similar myelin values, with small and explainable differences that did not impact the ability to distinguish the two patient groups.

Conclusion: The proposed hybrid multicore approach represents a more efficient alternative to MATLAB, especially for large-scale batch processing.