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

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.

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Chan, K, Author
Ziegs, T1, 2, Author           
Henning, A1, 2, Author           
Affiliations:
1Research Group MR Spectroscopy and Ultra-High Field Methodology, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528692              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 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.

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 Dates: 2020-08
 Publication Status: Published online
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Title: 2020 ISMRM & SMRT Virtual Conference & Exhibition
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Start-/End Date: 2020-08-08 - 2020-08-14

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Title: 2020 ISMRM & SMRT Virtual Conference & Exhibition
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 2887 Start / End Page: - Identifier: -