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Abstract #0499

NLINV-Net: Self-Supervised End-2-End Learning for Reconstructing Undersampled Radial Cardiac Real-Time Data

Moritz Blumenthal1, Guanxiong Luo1, Martin Schilling1, Markus Haltmeier2, and Martin Uecker1,3,4
1University Medical Center Göttingen, Göttingen, Germany, 2Department of Mathematics, University of Innsbruck, Innsbruck, Austria, 3Institute of Medical Engineering, Graz University of Technology, Graz, Austria, 4DZHK (German Centre for Cardiovascular Research), Göttingen, Germany


In this work, we propose NLINV-Net, a neural network architecture for jointly estimating the image and coil sensitivity maps of radial cardiac real-time data. NLINV-Net is inspired by NLINV and solves the non-linear formulation of the SENSE inverse problem by unrolling the iteratively regularized Gauss-Newton method, which is improved by adding neural network based regularization terms. NLINV-Net is trained in a self-supervised fashion, which is crucial for cardiac real-time data which lack any ground truth reference. NLINV-Net significantly reduces noise and streaking artifacts compared to reconstructions using plain NLINV.

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