Abstract #1818

Accelerating Ultra-Low Field MRI with Compressed Sensing

David E J Waddington¹, Efrat Shimron², Nicholas Hindley^1,3, Neha Koonjoo³, and Matthew S Rosen^3,4,5

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

Synopsis

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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