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

@entity19 @entity1 @entity258 @entity6470
PURPOSE: Brief bursts of @entity6470 noise during MR data acquisition ("k-space spikes") cause disruptive image artifacts, manifesting as stripes overlaid on the image. @entity6470 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 @entity258 . Automated center refilling was applied to keep the peaked central cluster of k-space from misclassification in the sparse component. RESULTS: This algorithm was demonstrated to effectively remove k-space spikes from four data types under conditions generating spikes: (i) @entity19 heart T1 mapping, (ii) @entity19 heart cine imaging, (iii) @entity1 kidney diffusion tensor imaging (DTI) data, and (iv) @entity1 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. CONCLUSION: 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, 2015.    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.
