Using the robust principal component analysis algorithm to remove RF spike artifacts from MR images.

ABSTRACT:

Purpose

Brief bursts of RF noise during MR data acquisition ("k-space spikes") cause disruptive image artifacts, manifesting as stripes overlaid on the image. RF noise is often related to hardware problems, including vibrations during gradient-heavy sequences, such as diffusion-weighted imaging. In this study, we present an application of the Robust Principal Component Analysis (RPCA) algorithm to remove spike noise from k-space.

Methods

Corrupted k-space matrices were decomposed into their low-rank and sparse components using the RPCA algorithm, such that spikes were contained within the sparse component and artifact-free k-space data remained in the low-rank component. Automated center refilling was applied to keep the peaked central cluster of k-space from misclassificati

SUBMITTER: Campbell-Washburn AE 

PROVIDER: S-EPMC4720596 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature