Noise-reduction techniques for 1H-FID-MRSI at 14.1T: Monte-Carlo validation in vivo application
arxiv(2023)
摘要
Proton magnetic resonance spectroscopic imaging (1H-MRSI) is a powerful tool
that enables the multidimensional non-invasive mapping of the neurochemical
profile at high-resolution over the entire brain. The constant demand for
higher spatial resolution in 1H-MRSI led to increased interest in
post-processing-based denoising methods aimed at reducing noise variance. The
aim of the present study was to implement two noise-reduction techniques, the
Marchenko-Pastur principal component analysis (MP-PCA) based denoising and the
low-rank total generalized variation (LR-TGV) reconstruction, and to test their
potential and impact on preclinical 14.1T fast in vivo 1H-FID-MRSI datasets.
Since there is no known ground truth for in vivo metabolite maps, additional
evaluations of the performance of both noise-reduction strategies were
conducted using Monte-Carlo simulations. Results showed that both denoising
techniques increased the apparent signal-to-noise ratio SNR while preserving
noise properties in each spectrum for both in vivo and Monte-Carlo datasets.
Relative metabolite concentrations were not significantly altered by either
methods and brain regional differences were preserved in both synthetic and in
vivo datasets. Increased precision of metabolite estimates was observed for the
two methods, with inconsistencies noted on lower concentrated metabolites. Our
study provided a framework on how to evaluate the performance of MP-PCA and
LR-TGV methods for preclinical 1H-FID MRSI data at 14.1T. While gains in
apparent SNR and precision were observed, concentration estimations ought to be
treated with care especially for low-concentrated metabolites.
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