Robust and Automated Method for Spike Detection and Removal in Magnetic Resonance Imaging

TL;DR

This paper introduces an automated method for detecting and correcting RF spike noise in MRI images, significantly improving image quality by accurately identifying and removing spike artifacts without human intervention.

Contribution

The authors developed a novel spike detection and correction technique using k-space analysis and compressed sensing, achieving high accuracy in both simulated and real MRI data.

Findings

High detection accuracy with Matthews correlation coefficients above 0.95

Near-perfect correction with specificities above 0.9994

Effective in both synthetic and real MRI datasets

Abstract

Radio frequency (RF) spike noise is a common source of exogenous image corruption in MRI. Spikes occur as point-like disturbances of $k$-space that lead to global sinusoidal intensity errors in the image domain. Depending on the amplitude of the disturbances and their locations in $k$-space, the effect of a spike can be significant, often ruining the reconstructed images. Here we present both a spike detection method and a related data correction method for automatic correction of RF spike noise. To detect spikes, we found the $k$-space points that have the most significant effect on the total variation of the image. To replace the spikes, we used a compressed sensing reconstruction in which only the points thought to be corrupted are unconstrained. We demonstrated our technique in two cases: (1) in vivo gradient echo brain data with artificially corrupted points and (2) actual, complex scanner data from a whole-body fat-water imaging gradient echo protocol corrupted by spikes at uncertain locations. Our method allowed near-perfect detection and correction with no human intervention. We calculated Matthews correlation coefficients and sensitivities above 0.95 for a maximum of 0.78\% corruption in synthetically corrupted in vivo brain data. We also found specificities above 0.9994.