Owen L. Kaluza1,
Amanda C. L. Ng2, 3, David K. Wright4,
5, Leigh A. Johnston, 56, John Grundy7, David G.
Barnes2
1Monash
e-Research Centre, Monash University, Clayton, Victoria, Australia; 2Monash
Biomedical Imaging, Monash University, Clayton, Victoria, Australia; 3Department
of Electrical & Electronic Engineering, The University of Melbourne,
Parkville, Victoria, Australia; 4Centre for Neuroscience, The
University of Melbourne, Parkville, Victoria, Australia; 5Florey
Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia; 6NeuroEngineering
Laboratory, Dept. Electrical & Electronic Engineering, The University of
Melbourne, Parkville, Victoria, Australia; 7Centre for Complex
Software Systems and Services, Swinburne University of Technology, Hawthorn,
Victoria, Australia
Diffusion-guided quantitative susceptibility mapping (QSM) is a new technique that promises improved mapping without the need for multiple-orientation (COSMOS) image acquisitions. However, the computation time for realistic image sizes on central-processing unit (CPU)-based supercomputers is prohibitively expensive. We have analysed the dQSM algorithm and developed an OpenCL-based implementation that runs on graphics processing unit (GPU)-based compute clusters. Our implementation yields identical results to the parallel CPU code, in drastically less time. Dual-GPU cluster nodes can compute the dQSM map 8 - 10 times faster when their GPUs are used compared to their multi-core CPUs. With this work, use of dQSM in research imaging facilities becomes practicable on quite modest computational facilities.