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

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Henning,  A
Research Group MR Spectroscopy and Ultra-High Field Methodology, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Chan, K., & Henning, A. (2021). Highly accelerated variable-density MultiNet CAIPIRINHA for 1H MRSI and augmented MRSI neural network training. Poster presented at 2021 ISMRM & SMRT Annual Meeting & Exhibition (ISMRM 2021).


Cite as: http://hdl.handle.net/21.11116/0000-0008-864D-B
Abstract
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.