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Abstract #0555

ResoNet: Physics Informed Deep Learning based Off-Resonance Correction Trained on Synthetic Data

Alfredo De Goyeneche 1, Shreya Ramachandran1, Ke Wang1,2, Ekin Karasan1, Stella Yu1,2, and Michael Lustig1
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, 2International Computer Science Institute, University of California, Berkeley, Berkeley, CA, United States


We propose a physics-inspired, unrolled-deep-learning framework for off-resonance correction. Our forward model includes coil sensitivities, multi-frequency bins, and non-uniform Fourier transforms hence compatible with fat/water imaging and parallel imaging acceleration. The network, which includes data-consistency terms and CNN modules serving as proximal operators, is trained end-to-end using only synthetic random field maps, coil sensitivities, and noise-like images with statistics (smoothness) mimicking natural signals. Our aim is to train the network to reverse off-resonance irrespective of the type of imaging, and hence generalizable to any anatomy and contrast without retraining. We demonstrate initial results in simulations, phantom, and in-vivo data.

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