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Tytuł pozycji:

PCA denoising and Wiener deconvolution of 31 P 3D CSI data to enhance effective SNR and improve point spread function.

Tytuł:
PCA denoising and Wiener deconvolution of P 3D CSI data to enhance effective SNR and improve point spread function.
Autorzy:
Froeling M; Department of Radiology, Imaging Division, University Medical Center Utrecht, Utrecht, The Netherlands.
Prompers JJ; Department of Radiology, Imaging Division, University Medical Center Utrecht, Utrecht, The Netherlands.
Klomp DWJ; Department of Radiology, Imaging Division, University Medical Center Utrecht, Utrecht, The Netherlands.
van der Velden TA; Department of Radiology, Imaging Division, University Medical Center Utrecht, Utrecht, The Netherlands.
Źródło:
Magnetic resonance in medicine [Magn Reson Med] 2021 Jun; Vol. 85 (6), pp. 2992-3009. Date of Electronic Publication: 2021 Feb 01.
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Publication: 1999- : New York, NY : Wiley
Original Publication: San Diego : Academic Press,
MeSH Terms:
Algorithms*
Magnetic Resonance Imaging*
Principal Component Analysis ; Signal-To-Noise Ratio
References:
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Contributed Indexing:
Keywords: deconvolution; denoising; metabolic imaging; phosphorus chemical shift imaging; phosphorus magnetic resonance spectroscopy
Entry Date(s):
Date Created: 20210201 Date Completed: 20210520 Latest Revision: 20210520
Update Code:
20240104
PubMed Central ID:
PMC7986807
DOI:
10.1002/mrm.28654
PMID:
33522635
Czasopismo naukowe
Purpose: This study evaluates the performance of 2 processing methods, that is, principal component analysis-based denoising and Wiener deconvolution, to enhance the quality of phosphorus 3D chemical shift imaging data.
Methods: Principal component analysis-based denoising increases the SNR while maintaining spectral information. Wiener deconvolution reduces the FWHM of the voxel point spread function, which is increased by Hamming filtering or Hamming-weighted acquisition. The proposed methods are evaluated using simulated and in vivo 3D phosphorus chemical shift imaging data by 1) visual inspection of the spatial signal distribution; 2) SNR calculation of the PCr peak; and 3) fitting of metabolite basis functions.
Results: With the optimal order of processing steps, we show that the effective SNR of in vivo phosphorus 3D chemical shift imaging data can be increased. In simulations, we show we can preserve phosphorus-containing metabolite peaks that had an SNR < 1 before denoising. Furthermore, using Wiener deconvolution, we were able to reduce the FWHM of the voxel point spread function with only partially reintroducing Gibb-ringing artifacts while maintaining the SNR. After data processing, fitting of the phosphorus-containing metabolite signals improved.
Conclusion: In this study, we have shown that principal component analysis-based denoising in combination with regularized Wiener deconvolution allows increasing the effective spectral SNR of in vivo phosphorus 3D chemical shift imaging data, with reduction of the FWHM of the voxel point spread function. Processing increased the effective SNR by at least threefold compared to Hamming weighted acquired data and minimized voxel bleeding. With these methods, fitting of metabolite amplitudes became more robust with decreased fitting residuals.
(© 2021 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

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