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Abstract #1998

SNR-Enhancing Reconstruction for Multi-TE MRSI Using a Learned Nonlinear Low-dimensional Model

Yahang Li1,2, Zepeng Wang1,2, and Fan Lam1,2
1Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, United States, 2Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, United States

We report a new method for SNR-enhancing reconstruction of multi-TE MRSI data. Specifically, we designed a deep complex convolutional autoencoder (DCCAE) to learn a nonlinear low-dimensional model of the high-dimensional multi-TE spectra which allowed for effective separation of molecular signals and noise. A constrained reconstruction formulation is used to incorporate the learned model for denoising spatial-temporal reconstruction. The performance of the learned model and the proposed reconstruction method have been evaluated using both simulation and experimental multi-TE $$$^1$$$H-MRSI data. Results obtained demonstrate superior denoising performance achieved by the proposed method over alternative spatial-spectrally constrained denoising strategies.

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