In this study a novel method for the denoising of CEST MRI data is presented, combining the formation of subsets of similar spectra and the subsequent application of a principal component analysis. Exploiting only the subtle spectral differences of these reduced datasets – as opposed to using all spectra for the analysis – allows for a better identification and isolation of the obscured underlying spectral features. The proposed denoising resulted in an SNR gain by approximately a factor of four compared to the noisy initial data and an additional 14% compared to the conventional principal component analysis denoising.
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