Clinical NeurosciencePerformance gains with Compute Unified Device Architecture-enabled eddy current correction for diffusion MRI.Maller, Jerome J.a,,b,,c; Grieve, Stuart M.b,,d; Vogrin, Simon J.e; Welton, ThomasbAuthor Information aGeneral Electric Healthcare, Victoria bImaging and Phenotyping Laboratory, Charles Perkins Centre, Faculty of Medicine and Health, The University of Sydney, NSW cDepartment of Psychiatry, Monash Alfred Psychiatry Research Centre, Victoria dDepartment of Radiology, Royal Prince Alfred Hospital, Camperdown, Sydney, NSW eDepartment of Neurology, Centre for Clinical Neuroscience and Neurological Research, St Vincent’s Hospital, Fitzroy, Victoria, Australia Received 5 April 2020 Accepted 18 April 2020 Correspondence to Jerome J. Maller, PhD, Sydney Translational Imaging Laboratory, Heart Research Institute, Charles Perkins Centre, University of Sydney, Sydney, NSW 2050, Australia, Tel: +(02) 8627 1616; e-mail: email@example.com NeuroReport: July 10, 2020 - Volume 31 - Issue 10 - p 746-753 doi: 10.1097/WNR.0000000000001475 Buy Metrics Abstract Correcting for eddy currents, movement-induced distortion and gradient inhomogeneities is imperative when processing diffusion MRI (dMRI) data, but is highly computing resource-intensive. Recently, Compute Unified Device Architecture (CUDA) was implemented for the widely-used eddy-correction software, ‘eddy’, which reduces processing time and allows more comprehensive correction. We investigated processing speed, performance and compatibility of CUDA-enabled eddy-current correction processing compared to commonly-used non-CUDA implementations. Four representative dMRI datasets from the Human Connectome Project, Alzheimer’s Disease Neuroimaging Initiative and Chronic Diseases Connectome Project were processed on high-specification and regular workstations through three different configurations of ‘eddy’. Processing times and graphics processing unit (GPU) resources used were monitored and compared. Using CUDA reduced the ‘eddy’ processing time by a factor of up to five. The CUDA slice-to-volume correction method was also faster than non-CUDA eddy except when datasets were large. We make a series of recommendations for eddy configuration and hardware. We suggest that users of eddy-correction software for dMRI processing utilise CUDA and take advantage of the slice-to-volume correction option. We recommend that users run eddy on computers with at least 32GB motherboard random access memory (RAM), and a graphics card with at least 4.5GB RAM and 3750 cores to optimise processing time. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.