Phase Image Texture Analysis for Motion Detection in Diffusion MRI (PITA-MDD)

• Published in: Magnetic resonance imaging 2019-10, Vol.62, p.228-241
• Main Authors: Elsaid, Nahla M.H., Prince, Jerry L., Roys, Steven, Gullapalli, Rao P., Zhuo, Jiachen
• Format: Article
• Language: English
• Subjects: Algorithms; Anisotropy; Brain - diagnostic imaging; Diffusion Magnetic Resonance Imaging; Humans; Image Processing, Computer-Assisted - methods; Motion; Prospective Studies; Reproducibility of Results
• ISSN: 0730-725X; 1873-5894
• DOI: 10.1016/j.mri.2019.07.009

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. 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. 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. 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.