Jo Schlemper^{1}, Seyed Sadegh Mohseni Salehi^{1}, Carole Lazarus^{1}, Hadrien Dyvorne^{1}, Rafael O'Halloran^{1}, Nicholas de Zwart^{1}, Laura Sacolick^{1}, Samantha By^{1}, Joel M. Stein^{2}, Daniel Rueckert^{3}, Michal Sofka^{1}, and Prantik Kundu^{1,4}

^{1}Hyperfine Research Inc., Guilford, CT, United States, ^{2}Hospitals of the University of Pennsylvania, Philadelphia, PA, United States, ^{3}Computing, Imperial College London, London, United Kingdom, ^{4}Icahn 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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The architecture of Nonuniform Variational Network. Each block-$$$i$$$ has three components: CNN-$$$i$$$, DC-$$$i$$$ and $$$\lambda$$$. CNN-$$$i$$$ is a small network inspired by U-net to image de-aliasing, DC-$$$i$$$ is a non-Cartesian DC layer and $$$\lambda$$$ is the weighting term. The model has 5 blocks in total, which is end-to-end trainable. For more mathematically rigorous detail, refer to reference 28.

The quantitative results for the simulated data acquisition with system imperfections and the acceleration factor of 3.5. For each metric, mean and standard deviation are computed. The MSE's are scaled by 10^{3}. The proposed approach consistently outperformed the baseline approaches (U-net and k-space U-net). In particular, U-net alone was not powerful enough to correct all aliasing and $$$k$$$-space U-net struggled to converge, potentially due to the jittering of $$$k$$$-space samples.

The reconstructions for the T1-weighted (bottom half) and T2-weighted images (top half) from the simulated data acquisition with system imperfections and the acceleration factor of 3.5. Top rows show the reconstructions and the bottom rows show the corresponding error maps. The reconstructions from NVN resulted in sharper images with highest data fidelities, owing to the data consistency blocks. U-net based methods could not resolve all aliasing artifacts correctly.

Linear (top), NVN (middle) and CS (bottom) reconstructions of prospectively undersampled low-field (64mT) images. From left: FLAIR and T2-weighted images from healthy controls, T2-weighted image from a patient with hydrocephalus, and T2-weighted image from a patient with a medial left thalamic cystic tumor and shunted hydrocephalus. Note the increased definition of NVN and CS, while NVN further reduced noise-like effect of CS.