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.
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 sclerosispatients 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.
Authors: Tonima Sumya Ali; Thorarin Albert Bjarnason; Donna L Senger; Jeff F Dunn; Jeffery T Joseph; Joseph Ross Mitchell Journal: J Med Imaging (Bellingham) Date: 2015-07-21
Authors: Adam V Dvorak; Emil Ljungberg; Irene M Vavasour; Hanwen Liu; Poljanka Johnson; Alexander Rauscher; John L K Kramer; Roger Tam; David K B Li; Cornelia Laule; Laura Barlow; Hannah Briemberg; Alex L MacKay; Anthony Traboulsee; Piotr Kozlowski; Neil Cashman; Shannon H Kolind Journal: Neuroimage Clin Date: 2019-06-17 Impact factor: 4.881
Authors: Jonathan O'Muircheartaigh; Irene Vavasour; Emil Ljungberg; David K B Li; Alexander Rauscher; Victoria Levesque; Hideki Garren; David Clayton; Roger Tam; Anthony Traboulsee; Shannon Kolind Journal: Hum Brain Mapp Date: 2019-01-15 Impact factor: 5.038
Authors: Lisa Eunyoung Lee; Irene M Vavasour; Adam Dvorak; Hanwen Liu; Shawna Abel; Poljanka Johnson; Stephen Ristow; Shelly Au; Cornelia Laule; Roger Tam; David Kb Li; Helen Cross; Nathalie Ackermans; Alice J Schabas; Jillian Chan; Ana-Luiza Sayao; Virginia Devonshire; Robert Carruthers; Anthony Traboulsee; Shannon Kolind Journal: Mult Scler Date: 2021-03-22 Impact factor: 6.312