SNR Enhancement for Multi-TE MRSI Using Joint Low-Dimensional Model and Spatial Constraints.

ABSTRACT: We present a novel method to enhance the SNR for multi-TE MR spectroscopic imaging (MRSI) data by integrating learned nonlinear low-dimensional model and spatial constraints. A deep complex convolutional autoencoder (DCCAE) was developed to learn a nonlinear low-dimensional representation of the high-dimensional multi-TE ¹H spectroscopy signals. The learned model significantly reduces the data dimension thus serving as an effective constraint for noise reduction. A reconstruction formulation was proposed to integrate the spatiospectral encoding model, the learned model, and a spatial constraint for an SNR-enhancing reconstruction from multi-TE data. The proposed method has been evaluated using both numerical simulations and in vivo brain MRSI experiments. The superior denoising

SUBMITTER: Li Y 

PROVIDER: S-EPMC9514378 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature