An Algorithm for Detecting Short-Lived Motion-Induced Corruption in k-Space

Motivation: Motion-caused short-lived data inconsistencies present challenges for artifact reduction in magnetic resonance imaging. Goal(s): Development of a method for automatically detecting motion-corrupted k-space lines in MRI data. Approach: The proposed approach utilizes multiple statistical multiscale testing to identify motion-corrupted data. Results: The method successfully detected corruptions in both simulated and in-vivo MRI data, enhancing the potential for artifact reduction in motion-sensitive MRI applications. Impact: We propose a method to automatically detect k-space lines, corrupted by patient motion, using a multiscale scanning statistic, allowing to replace these data in optimized reconstructions.