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 Uecker^1,3,4

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

Synopsis

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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