ArticleMagnetic resonance imaging2019
Phase Image Texture Analysis for Motion Detection in Diffusion MRI (PITA-MDD).
Article in Magnetic resonance imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Analysis of Tongue Muscle Strain During Speech From Multimodal Magnetic Resonance Imaging.Journal of speech, language, and hearing research : JSLHR · 2023Article
- Validation of Muscle Fiber Architecture of the Human Tongue Revealed by Diffusion Magnetic Resonance Imaging With Histology Verification.Journal of speech, language, and hearing research : JSLHR · 2022Article
- Prospective motion detection and re-acquisition in diffusion MRI using a phase image-based method-Application to brain and tongue imaging.Magnetic resonance in medicine · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
purposePronounced 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.
methodsThis 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.
resultsUsing 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.
conclusionIn 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.
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