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

ABSTRACT:

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

SUBMITTER: Froeling M 

PROVIDER: S-EPMC7986807 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature