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Abstract #1999

Highly accelerated variable-density MultiNet CAIPIRINHA for 1H MRSI and augmented MRSI neural network training

Kimberly Chan1 and Anke Henning1,2
1The University of Texas Southwestern, Dallas, TX, United States, 2Max Planck Institute for Biological Cybernetics, Tübingen, Germany

It has previously been shown that neural networks combined with variable k-space undersampling (MultiNet GRAPPA) is superior to a conventional GRAPPA reconstruction and is feasible at 7T. Here, MultiNet reconstruction of several new CAIPIRINHA-based variable-density k-space undersampling schemes is investigated. A new approach to train the neural networks (NN) by augmenting the MRSI data with the non-water suppressed (NWS) data to provide additional self-calibration training data is also introduced and evaluated. In this study, both are shown here to reduce lipid artifacts and improve metabolic maps at high acceleration factors relative to those previously proposed for MultiNet GRAPPA.

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