Most clinical MRI scanners operate at high magnetic field, however low-field MRI offers many advantages and promises to improve the value of MRI. The main drawback is low SNR; several signal averages are often required, which may result in prohibitively long scans. We can look to deep learning (DL) to facilitate accelerated low-field imaging through both denoising and sparse sampling. In this work, we use a variational network for both denoising and under-sampled reconstruction of brain images acquired on a 0.55T prototype system, demonstrating that low-field MRI paired with DL can produce high-quality images in very short scan times.
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