Tensor denoising of multidimensional MRI data.

ABSTRACT:

Purpose

To develop a denoising strategy leveraging redundancy in high-dimensional data.

Theory and methods

The SNR fundamentally limits the information accessible by MRI. This limitation has been addressed by a host of denoising techniques, recently including the so-called MPPCA: principal component analysis of the signal followed by automated rank estimation, exploiting the Marchenko-Pastur distribution of noise singular values. Operating on matrices comprised of data patches, this popular approach objectively identifies noise components and, ideally, allows noise to be removed without introducing artifacts such as image blurring, or nonlocal averaging. The MPPCA rank estimation, however, relies on a large number of noise singular values relative to the number of signal com

SUBMITTER: Olesen JL 

PROVIDER: S-EPMC10092037 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature