Abstract #3780

Correction of RF Spike Noise in MR Images Using Robust Principal Component Analysis

Adrienne E. Campbell-Washburn¹,², David Atkinson³, Oliver Josephs⁴, Mark F. Lythgoe⁵, Roger J. Ordidge⁶, David L. Thomas⁷

¹Centre for Advnaced Biomedical Imaging, University College London, London, United Kingdom; ²Department of Medical Physics and Bioengineering, University College London, London, United Kingdom; ³Centre for Medical Imaging and Centre for Medical Image Computing, University College London, London, United Kingdom; ⁴University College London and Birkbeck College, London, United Kingdom; ⁵Centre for Advanced Biomedical Imaging, Division of Medicine and Institute of Child Health, University College London, London, United Kingdom; ⁶Centre for Neuroscience, University of Melbourne, Melbourne, Victoria, Australia; ⁷Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London, London, United Kingdom

RF spike noise, caused by hardware problems, can lead to striping artefacts in MR images. These artefacts affect the image quality and quantitative information from the MRI data, and often must be removed in post-processing. This abstract presents an algorithm for semi-automated detection and correction of RF spike noise based on Robust Principal Component Analysis (RPCA). RPCA is used to decompose the measured k-space into low-rank (artefact-free) and sparse (RF spike) matrix components, including an automatic correction for the misidentification of the central k-space cluster. This algorithm is demonstrated to efficiently and effectively recover artefact-free data and regain quantitative information.