Elsaid, Nahla M. H.Prince, Jerry L.Roys, StevenGullapalli, Rao P.Zhuo, Jiachen2019-08-302019-08-302019-10Elsaid, N. M. H., Prince, J. L., Roys, S., Gullapalli, R. P., & Zhuo, J. (2019). Phase image texture analysis for motion detection in diffusion MRI (PITA-MDD). Magnetic Resonance Imaging, 62, 228-241. https://doi.org/10.1016/j.mri.2019.07.009https://hdl.handle.net/1805/20712Purpose Pronounced spin phase artifacts appear in diffusion-weighted imaging (DWI) with only minor subject motion. While DWI data corruption is often identified as signal drop out in diffusion-weighted (DW) magnitude images, DW phase images may have higher sensitivity for detecting subtle subject motion. Methods This article describes a novel method to return a metric of subject motion, computed using an image texture analysis of the DW phase image. This Phase Image Texture Analysis for Motion Detection in dMRI (PITA-MDD) method is computationally fast and reliably detects subject motion from diffusion-weighted images. A threshold of the motion metric was identified to remove motion-corrupted slices, and the effect of removing corrupted slices was assessed on the reconstructed FA maps and fiber tracts. Results Using a motion-metric threshold to remove the motion-corrupted slices results in superior fiber tracts and fractional anisotropy maps. When further compared to a state-of-the-art magnitude-based motion correction method, PITA-MDD was able to detect comparable corrupted slices in a more computationally efficient manner. Conclusion In this study, we evaluated the use of DW phase images to detect motion corruption. The proposed method can be a robust and fast alternative for automatic motion detection in the brain with multiple applications to inform prospective motion correction or as real-time feedback for data quality control during scanning, as well as after data is already acquired.enPublisher PolicyDW phase imagesPITA-MDDmotion corruptionArticle