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Abstract #0991

Deep Learning MRI Reconstruction in Application to Point-of-Care MRI

Jo Schlemper1, Seyed Sadegh Mohseni Salehi1, Carole Lazarus1, Hadrien Dyvorne1, Rafael O'Halloran1, Nicholas de Zwart1, Laura Sacolick1, Samantha By1, Joel M. Stein2, Daniel Rueckert3, Michal Sofka1, and Prantik Kundu1,4
1Hyperfine Research Inc., Guilford, CT, United States, 2Hospitals of the University of Pennsylvania, Philadelphia, PA, United States, 3Computing, Imperial College London, London, United Kingdom, 4Icahn School of Medicine at Mount Sinai, New York City, NY, United States

The goal of low-field (64 mT) portable point-of-care (POC) MRI is to produce low cost, clinically acceptable MR images in reasonable scan times. However, non-ideal MRI behaviors make the image quality susceptible to artifacts from system imperfections and undersampling. In this work, a deep learning approach is proposed for fast reconstruction from hardware and sampling-associated imaging artifacts. The proposed approach outperforms the reference deep learning approaches for retrospectively undersampled data with simulated system imperfections. Furthermore, we demonstrate that it yields better image quality and faster reconstruction than compressed sensing approach for unseen, prospectively undersampled low-field POC MR images.

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