Abstract
PurposeBrief 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 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) 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. 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 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.
Highlights
Artifacts in MR images degrade image quality and can affect the quantitative values derived from the images
We have demonstrated that Robust Principal Component Analysis can successfully remove RF spike noise and reduce detrimental stripe artifacts in MRI images in a postprocessing algorithmic step
Using T1 mapping and diffusion tensor imaging (DTI) data, we demonstrated that quantitative parameter values are maintained in artifact free datasets and recovered from spike-corrupted datasets when applying this algorithm
Summary
Artifacts in MR images degrade image quality and can affect the quantitative values derived from the images. Short bursts of random RF noise create high intensity spikes in k-space, resulting in stripes across the image, the exact appearance of which depends on the location of the spike in k-space. This artifact is prominent in gradient-heavy sequences such as diffusion tensor imaging (DTI) [1]. Imperfections in hardware should be addressed promptly; the source of the RF spikes is notoriously difficult to find, and in practice, data is sometimes acquired when these faults are present and the resulting artifacts must be removed in image postprocessing. We sought to develop an alternative method that is semiautomated and can reliably remove image artifacts for a broad range of data types
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