Abstract #0305

Accelerated Imaging at Ultralow Magnetic Fields: A comparative study of traditional and neural-network-based reconstruction approaches

David E. J. Waddington¹, Efrat Shimron², Shanshan Shan¹, Neha Koonjoo³, Sheng Shen³, and Matthew S. Rosen^3,4,5

¹Image X Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia, ²Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, CA, United States, ³A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁴Harvard Medical School, Boston, MA, United States, ⁵Department of Physics, Harvard University, Cambridge, MA, United States

Synopsis

Keywords: Low-Field MRI, Image Reconstruction

Portable MRI scanners that operate at very low magnetic fields are increasingly being deployed in clinical settings. However, accelerated acquisition and reconstruction methods that boost the quality of low-field MR images are needed to improve the diagnostic accuracy of the modality. Here, we compare leading data-driven and model-driven deep learning frameworks to compressed sensing (CS) for the reconstruction of undersampled ultralow field MRI data, finding that neural network approaches can boost quantitative image reconstruction metrics.

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