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  LeaRning nonlineAr representatIon and projectIon for faSt constrained MRSI rEconstruction (RAIISE)

Li, Y., Ruhm, L., Henning, A., & lam, F. (2022). LeaRning nonlineAr representatIon and projectIon for faSt constrained MRSI rEconstruction (RAIISE). Poster presented at Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022), London, UK.

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Li, Y, Author
Ruhm, L1, Author              
Henning, A1, Author              
lam, F, Author
Affiliations:
1Research Group MR Spectroscopy and Ultra-High Field Methodology, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528692              

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 Abstract: We proposed here a novel method for computationally efficient reconstruction from noisy MRSI data. The proposed method is characterized by (a) a strategy that jointly learns a nonlinear low-dimensional representation of high-dimensional spectroscopic signals and a projector to recover the low-dimensional embeddings from noisy FIDs; and (b) a formulation that integrates forward encoding model, a spectral constraint from the learned representation and a complementary spatial constraint. The learned projector allows for the derivation of a highly efficient algorithm combining projected gradient descent and ADMM. The proposed method has been evaluated using simulation and in vivo data, demonstrating impressive SNR-enhancing performance.

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 Dates: 2022-05
 Publication Status: Published online
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Title: Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022)
Place of Event: London, UK
Start-/End Date: 2022-05-07 - 2022-05-12

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Title: Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 4808 Start / End Page: - Identifier: -