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 Muthurangu^4,5

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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