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Area of Science:

  • Medical Imaging
  • Neuroimaging
  • Diffusion MRI

Background:

  • Diffusion-weighted imaging (DWI) is susceptible to spin phase artifacts caused by minor subject motion.
  • Signal dropout in DW magnitude images is a common indicator of DWI data corruption.
  • DW phase images may offer higher sensitivity for detecting subtle motion artifacts.

Purpose of the Study:

  • To introduce a novel method for quantifying subject motion using diffusion-weighted phase images.
  • To assess the effectiveness of this method in detecting and mitigating motion-corrupted slices in DWI data.

Main Methods:

  • Developed Phase Image Texture Analysis for Motion Detection in dMRI (PITA-MDD) using DW phase image texture analysis.
  • Computed a motion metric to identify and remove motion-corrupted slices.
  • Evaluated the impact of removing corrupted slices on reconstructed fractional anisotropy (FA) maps and fiber tracts.

Main Results:

  • Removing motion-corrupted slices based on the PITA-MDD metric yielded superior fiber tracts and FA maps.
  • PITA-MDD demonstrated comparable corrupted slice detection to state-of-the-art magnitude-based methods.
  • The PITA-MDD method is computationally efficient.

Conclusions:

  • DW phase images are effective for detecting motion corruption in DWI.
  • The PITA-MDD method provides a robust and fast alternative for automatic motion detection in brain imaging.
  • This method has potential applications in prospective motion correction, real-time quality control, and post-acquisition data refinement.