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Abstract #2887

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

Kimberly Chan1, Theresia Ziegs2, and Anke Henning1,2
1Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States, 2Max Planck Institute for Biological Cybernetics, Tuebingen, Germany

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.

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