Denoising complex-valued diffusion MR images using a two-step non-local principal component analysis approach.

ABSTRACT:

Purpose

to propose a two-step non-local principal component analysis (PCA) method and demonstrate its utility for denoising diffusion tensor MRI (DTI) with a few diffusion directions.

Methods

A two-step denoising pipeline was implemented to ensure accurate patch selection even with high noise levels and was coupled with data preprocessing for g-factor normalization and phase stabilization before data denoising with a non-local PCA algorithm. At the heart of our proposed pipeline was the use of a data-driven optimal shrinkage algorithm to manipulate the singular values in a way that would optimally estimate the noise-free signal. Our approach's denoising performances were evaluated using simulation and in-vivo human data experiments. The results were compared to those obtaine

SUBMITTER: Ye X 

PROVIDER: S-EPMC11565869 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature