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Abstract #4831

Application of memory reduced NUFFT to multi-dimensional non-Cartesian MRI

Jyh-Miin Lin1, Grzegorz Kowalik 2, Javier Montalt Tordera1, Benoit Sarthou3, Philippe Ciuciu3, Jennifer Steeden4, and Vivek Muthurangu4,5

1Institute of Cardiovascular Science, University College London, London, United Kingdom, 2University College London, London, United Kingdom, 3CEA/NeuroSpin & INRIA-CEA Parietal team, Gif-sur-Yvette, France, 4Children's Cardiovascular Disease, University College London, London, United Kingdom, 5Great Ormond Street Hospital, London, United Kingdom

A precomputed interpolation matrix on a GPU has been commonly used for fast iterative NUFFT MRI reconstructions, but the size of a 3D interpolation matrix may exceed the memory available on a single GPU. We propose a memory reduced interpolation method that would reduce the size of a multidimensional non-Cartesian interpolation matrix on a GPU. The memory reduced NUFFT reduces the matrix size by more than 90%, while allowing a performance of 38% - 106% of the precomputed version. We also apply the memory reduced NUFFT to a large scale 3D generalized basis pursuit denoising algorithm (GBPDNA) reconstruction.

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