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Abstract #0602

On the Influence of Prior Knowledge in Learning Non-Cartesian 2D CINE Image Reconstruction

Kerstin Hammernik1, Gastao Cruz2, Thomas Kuestner2, Claudia Prieto2, and Daniel Rueckert1
1Department of Computing, Imperial College London, London, United Kingdom, 2School of Biomedical Engineering and Imaging Sciences, Kings College London, London, United Kingdom

In this work, we study the influence of prior knowledge in learning-based non-Cartesian 2D CINE MR image reconstruction. The proposed approach uses a novel minimal deep learning setup to embed the acquired non-Cartesian multi-coil data and conventional spatio-temporal (3D and 2D+t) Fields-of-Experts regularization in a proximal gradient variational network, achieving promising results for up to 12-fold retrospectively undersampled tiny golden-angle radial CINE imaging.

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