Principal component analysis fosr fast and model-free denoising of multi b-value diffusion-weighted MR images.

ABSTRACT: Despite the utility of tumour characterisation using quantitative parameter maps from multi-b-value diffusion-weighted MRI (DWI), clinicians often prefer the use of the image with highest diffusion-weighting (b-value), for instance for defining regions of interest (ROIs). However, these images are typically degraded by noise, as they do not utilize the information from the full acquisition. We present a principal component analysis (PCA) approach for model-free denoising of DWI data. PCA-denoising was compared to synthetic MRI, where a diffusion model is fitted for each voxel and a denoised image at a given b-value is generated from the model fit. A quantitative comparison of systematic and random errors was performed on data simulated using several diffusion models (mono-exponential, bi-e

SUBMITTER: Gurney-Champion OJ 

PROVIDER: S-EPMC7655121 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature