Improved MultiNet GRAPPA performance with semi-synthetic calibration data for 
accelerated 1H FID MRSI at 7T

Chan, K; Ziegs, T; Henning, A

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Improved MultiNet GRAPPA performance with semi-synthetic calibration data for accelerated 1H FID MRSI at 7T

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Ziegs, T
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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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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https://www.ismrm.org/20/program_files/DP06-02.htm
(Abstract)

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Citation

Chan, K., Ziegs, T., & Henning, A. (2020). Improved MultiNet GRAPPA performance with semi-synthetic calibration data for accelerated 1H FID MRSI at 7T. Poster presented at 2020 ISMRM & SMRT Virtual Conference & Exhibition.

Cite as: https://hdl.handle.net/21.11116/0000-0006-D8D6-5

Abstract

It has been shown that neural networks combined with variable k-space undersampling (MultiNet GRAPPA) is superior to a conventional GRAPPA reconstruction at 9.4T. Here, the feasibility of performing MultiNet GRAPPA for 1H FID-MRSI at 7T is investigated with and without novel modifications to the original acquisition/reconstruction scheme. In this study, it is shown that MultiNet GRAPPA is shown to be feasible for 1H MRSI acceleration at 7T with a new k-space undersampling scheme for higher signal-to-noise and increased map reliability and use of a novel technique to increase SNR retention using semi-synthetic calibration data without an increase in acquisition time.