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

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.