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Abstract #2000

k-Space-based Coil Combination via Geometric Deep Learning for Reconstruction of non-Cartesian MR Spectroscopic Imaging Data

Stanislav Motyka1, Lukas Hingerl1, Bernhard Strasser1, Gilbert Hangel1, Eva Heckova1, Asan Agibetov2, Georg Dorffner2, and Wolfgang Bogner1
1Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Wien, Austria, 2Section for Artificial Intelligence and Decision Support (CeMSIIS), Medical University of Vienna, Wien, Austria

A new coil combination method of non-Cartesian kspace MRSI data based on Geometric deep learning is introduced and compared to the conventional image-based coil combination. MRSI data were represented as a graph and a shallow neural network was used to solve the coil combination task. The training data were based on in vivo data and the performance of the network was tested on volunteer data, whose data were never shown to the network. The results were similar to conventional image-domain based coil combination. Thus, a highly accelerated online reconstruction is feasible with this method.

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