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Abstract #1818

Accelerating Ultra-Low Field MRI with Compressed Sensing

David E J Waddington1, Efrat Shimron2, Nicholas Hindley1,3, Neha Koonjoo3, and Matthew S Rosen3,4,5
1ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia, 22Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, CA, United States, 3A. A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 4Department of Physics, Harvard University, Cambridge, MA, United States, 5Harvard Medical School, Boston, MA, United States


Portable MRI scanners that operate at very low magnetic fields are increasingly being deployed in clinical settings. However, the intrinsic low signal-to-noise (SNR) ratio of these low-field MRI scanners often necessitates many signal averages, and therefore excessively long acquisition times. Here we propose to improve SNR through optimized k-space undersampling and Compressed Sensing reconstruction. We demonstrate this approach for 6.5 mT ultra-low-field MRI using: (1) retrospective-subsampling experiments with 2x to 4x acceleration; (2) prospectively-subsampled data acquired from a human brain phantom with a 6.5mT MRI. The results exhibit a higher SNR than the traditional averaging method, without increasing scan time.

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