Paper Info
Title
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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{1}$</tex-math></inline-formula> H 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 performance of the proposed over alternative methods was demonstrated, both qualitatively and quantitatively. In vivo multi-TE data was used to assess the improved metabolite quantification reproducibility and accuracy achieved by the proposed method. We expect the proposed SNR-enhancing reconstruction to enable faster and/or higher-resolution multi-TE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{1}$</tex-math></inline-formula> H-MRSI of the brain, potentially useful for various clinical applications.
Year
DOI
Venue
2022
10.1109/TBME.2022.3161417
IEEE Transactions on Biomedical Engineering
Keywords
DocType
Volume
Algorithms,Brain,Magnetic Resonance Imaging,Magnetic Resonance Spectroscopy,Nonlinear Dynamics,Reproducibility of Results
Journal
69
Issue
ISSN
Citations 
10
0018-9294
0
PageRank 
References 
Authors
0.34
13
3
Name
Order
Citations
PageRank
Yahang Li100.34
Zepeng Wang200.34
Fan Lam300.34