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Abstract #2667

Fast Diffusion-Guided QSM Using Graphical Processing Units

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.

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Keywords

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