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

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. Th...

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Bibliographic Details
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
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Summary: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.
ISSN:0730-725X
1873-5894
DOI:10.1016/j.mri.2019.07.009