Adaptive denoising for chemical exchange saturation transfer MR imaging. (30th July 2019)

• Record Type: Journal Article
• Authors: Breitling, Johannes; Deshmane, Anagha; Goerke, Steffen; Korzowski, Andreas; Herz, Kai; Ladd, Mark E.; Scheffler, Klaus; Bachert, Peter; Zaiss, Moritz
• Is Part Of: NMR in biomedicine. Volume 32:Number 11(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-30
• Subjects: amide -- CEST -- denoising -- MRI -- principal component analysis -- rNOE -- singular value decomposition; Nuclear magnetic resonance -- Periodicals; Magnetic Resonance Spectroscopy -- Periodicals; 574
• DOI: 10.1002/nbm.4133
• Languages: English
• ISSNs: 0952-3480

Abstract

Abstract : High image signal–to–noise ratio (SNR) is required to reliably detect the inherently small chemical exchange saturation transfer (CEST) effects in vivo. In this study, it was demonstrated that identifying spectral redundancies of CEST data by principal component analysis (PCA) in combination with an appropriate data–driven extraction of relevant information can be used for an effective and robust denoising of CEST spectra. The relationship between the number of relevant principal components and SNR was studied on fitted in vivo Z–spectra with artificially introduced noise. Three different data–driven criteria to automatically determine the optimal number of necessary components were investigated. In addition, these criteria facilitate straightforward assessment of data quality that could provide guidance for CEST MR protocols in terms of SNR. Insights were applied to achieve a robust denoising of highly sampled low power Z–spectra of the human brain at 3 and 7 T. The median criterion provided the best estimation for the optimal number of components consistently for all three investigated artificial noise levels. Application of the denoising technique to in vivo data revealed a considerable increase in image quality for the amide and rNOE contrast with a considerable SNR gain. At 7 T the denoising capability was quantified to be comparable or even superior to an averaging of six measurements. The proposed denoising algorithm enables an efficient and robust denoising of CEST data by combining PCA with appropriate data–driven truncation criteria. With this generally applicable technique at hand, small CEST effects can be reliably detected without the need for repeated measurements. Abstract : In this study, identification of spectral redundancies of CEST data by principal component analysis was combined with an appropriate data–driven extraction of relevant information for an effective and robust denoising of CEST spectra. Automatic determination of the optimal number of necessary components was studied on fitted in vivo Z–spectra with artificially introduced noise. Application of the denoising technique to in vivo data yielded amide and rNOE contrast images comparable or even superior to an averaging of six measurements.