Miscellaneous 2019

Denoising of Z-spectra for stable CEST MRI using principal component analysis

{Chemical exchange saturation transfer (CEST) MRI allows for the indirect detection of low-concentration biomolecules by their saturation transfer to the abundant water pool. However, reliable quantification of CEST effects remains challenging and requires a high image signal-to-noise ratio. In this study, we show that principle component analysis can provide a denoising capability which is comparable or better than 6-fold averaging. Principle component analysis allows identifying similarities across all noisy Z-spectra, and thus, extracting the relevant information. The resulting denoised Z-spectra provide a more stable basis for quantification of selective CEST effects, without requiring additional measurements.}

Author(s): Breitling, J and Deshmane, A and Goerke, S and Herz, K and Ladd, M and Scheffler, K and Bachert, P and Zaiss, M
Book Title: ISMRM 27th Annual Meeting & Exhibition
Year: 2019
Bibtex Type: Miscellaneous (misc)
Electronic Archiving: grant_archive

BibTex

@misc{item_3054596,
  title = {{Denoising of Z-spectra for stable CEST MRI using principal component analysis}},
  booktitle = {{ISMRM 27th Annual Meeting \& Exhibition}},
  abstract = {{Chemical exchange saturation transfer (CEST) MRI allows for the indirect detection of low-concentration biomolecules by their saturation transfer to the abundant water pool. However, reliable quantification of CEST effects remains challenging and requires a high image signal-to-noise ratio. In this study, we show that principle component analysis can provide a denoising capability which is comparable or better than 6-fold averaging. Principle component analysis allows identifying similarities across all noisy Z-spectra, and thus, extracting the relevant information. The resulting denoised Z-spectra provide a more stable basis for quantification of selective CEST effects, without requiring additional measurements.}},
  year = {2019},
  slug = {item_3054596},
  author = {Breitling, J and Deshmane, A and Goerke, S and Herz, K and Ladd, M and Scheffler, K and Bachert, P and Zaiss, M}
}