title: Using the robust principal component analysis algorithm to remove RF spike artifacts from MR images.
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. Magn Reson Med 75:2517-2525, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
authors.affiliation: Centre for Advanced Biomedical Imaging, University College London, London, United Kingdom.|Division of Intramural Research, Cardiovascular and Pulmonary Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA.
authors.forename: Adrienne E
authors.initials: AE
authors.lastname: Campbell-Washburn
authors.affiliation: Centre for Medical Imaging, University College London, United Kingdom.
authors.forename: David
authors.initials: D
authors.lastname: Atkinson
authors.affiliation: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom.|Laboratory for Social and Neural Systems Research (SNS Lab), Department of Economics, University of Zurich, Zurich, Switzerland.
authors.forename: Zoltan
authors.initials: Z
authors.lastname: Nagy
authors.affiliation: Centre for Medical Imaging, University College London, United Kingdom.
authors.forename: Rachel W
authors.initials: RW
authors.lastname: Chan
authors.affiliation: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom.|Birkbeck-UCL Centre for Neuroimaging, Birkbeck College, London, United Kingdom.
authors.forename: Oliver
authors.initials: O
authors.lastname: Josephs
authors.affiliation: Centre for Advanced Biomedical Imaging, University College London, London, United Kingdom.
authors.forename: Mark F
authors.initials: MF
authors.lastname: Lythgoe
authors.affiliation: Department of Anatomy and Neuroscience, University of Melbourne, Melbourne, Australia.
authors.forename: Roger J
authors.initials: RJ
authors.lastname: Ordidge
authors.affiliation: Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London, London, United Kingdom.
authors.forename: David L
authors.initials: DL
authors.lastname: Thomas
mesh_terms: D000465:Algorithms; D000818:Animals; D016477:Artifacts; D001921:Brain / Q000000981:diagnostic imaging; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006321:Heart / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007668:Kidney / Q000000981:diagnostic imaging; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D051379:Mice; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted
keywords: robust principal component analysis; spike noise; stripe artifact