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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