Journal Highlight: Using the robust principal component analysis algorithm to remove RF spike artifacts from MR images

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  • Published: Jul 18, 2016
  • Author: spectroscopyNOW
  • Channels: Chemometrics & Informatics
thumbnail image: Journal Highlight: Using the robust principal component analysis algorithm to remove RF spike artifacts from MR images
The Robust Principal Component Analysis algorithm has been used to remove the brief bursts of RF noise during magnetic resonance data acquisition (k-space spikes) that cause disruptive image artifacts, without affecting the underlying signals.

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

Magnetic Resonance in Medicine, 2016, 75, 2517-2525
Adrienne E. Campbell-Washburn, David Atkinson, Zoltan Nagy, Rachel W. Chan, Oliver Josephs, Mark F. Lythgoe, Roger J. Ordidge and David L. Thomas

Abstract: 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. 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 misclassification in the sparse component. This algorithm was demonstrated to effectively remove k-space spikes from four data types under conditions generating spikes: (i) mouse heart T1 mapping, (ii) mouse heart cine imaging, (iii) human kidney diffusion tensor imaging (DTI) data, and (iv) human brain DTI data. Myocardial T1 values changed by 86.1 ± 171 ms following despiking, and fractional anisotropy values were recovered following despiking of DTI data. The RPCA despiking algorithm will be a valuable postprocessing method for retrospectively removing stripe artifacts without affecting the underlying signal of interest.

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