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Abstract #3474

Sparse sampling reconstruction and noise removal of 0.55T brain MRI using a learned variational network  

Patricia M. Johnson1, Zhang Le2, David Grodzki3, and Florian Knoll1
1Center for Biomedical Imaging, New York University, New york, NY, United States, 2Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China, 3Siemens Healthcare GmbH, Erlangen, Germany

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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