1 Centre for Advanced Visualisation and Interaction, Faculty of Humanities, Aarhus University, Aarhus University2 Department of Computer Science, Faculty of Science, Aarhus University, Aarhus University3 Department of Clinical Medicine - The Department of Oncology, Department of Clinical Medicine, Health, Aarhus University4 Division of Imaging Sciences, King’s College London5 Alexandra Instituttet A/S6 Institute of Child Health, University College London, UK7 Department of Clinical Medicine - The Department of Oncology, Department of Clinical Medicine, Health, Aarhus University8 Alexandra Instituttet A/S
The most commonly used algorithm for non-cartesian MRI reconstruction is the gridding algorithm . It consists of three steps: 1) convolution with a gridding kernel and resampling on a cartesian grid, 2) inverse FFT, and 3) deapodization. On the CPU the convolution step is the far most time consuming of the three steps (Table 1). Modern graphics cards (GPUs) can be utilised as a fast parallel processor provided that algorithms are reformulated in a parallel solution. The purpose of this work is to test the hypothesis, that a non-cartesian reconstruction can be efficiently implemented on graphics hardware giving a significant speedup compared to CPU based alternatives. We present a novel GPU implementation of the convolution step that overcomes the problems of memory bandwidth that has limited the speed of previous GPU gridding algorithms .
Proceedings of Ismrm Workshop on Non-cartesian Mri, 2007
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Workshop on Non-Cartesian MRI - International Society for Magnetic Resonance in Medicine, 2007