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

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Ruhm,  L
Research Group MR Spectroscopy and Ultra-High Field Methodology, 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;

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Citation

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.


Cite as: http://hdl.handle.net/21.11116/0000-000A-5CB5-3
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.