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Abstract #4808

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

Yahang Li1,2, Loreen Ruhm3,4, Anke Henning3,4, and Fan Lam1,2,5
1Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, United States, 2Beckman Institute for Advanced Science and Technology, Urbana, IL, United States, 3Advanced Imaging Research Center, University of Texas Southwestern Medical Center (UTSW), Dallas, TX, United States, 4Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 5Cancer Center at Illinois, Urbana, IL, United States


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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