Diffusion imaging with prospective motion correction and reacquisition.

Area of Science:

• Medical Imaging
• Neuroimaging
• Biophysics

Background:

• Subject motion is a significant source of artifacts in diffusion-weighted imaging (DWI).
• Slow motion causes data misalignment, while fast motion leads to signal dropout and erroneous diffusion-derived maps like fractional anisotropy (FA).

Purpose of the Study:

• To develop and validate a fully automatic method for correcting both slow and fast bulk subject motion artifacts in DWI.
• To improve the accuracy of diffusion-derived maps and fiber tracking by minimizing motion-related distortions.

Main Methods:

• A novel method combining prospective motion correction (PMC) adapted for DWI with an automatic image reacquisition scheme.
• PMC corrects slow bulk motion, while reacquisition targets images with signal dropout artifacts.
• Image reacquisition is determined automatically during acquisition, requiring no external devices or reference scans.

Main Results:

• The method effectively corrects slow bulk motion, reducing misalignment and image blurring with mean absolute residuals <0.6 mm for translation and <0.5° for rotation.
• Reacquisition of motion-affected images successfully eliminates signal dropout artifacts.
• Resulting diffusion maps and fiber tracking are free from motion-related artifacts.

Conclusions:

• The presented automatic method successfully reduces common motion-related artifacts in DWI.
• This technique enhances the reliability of diffusion imaging data and derived quantitative maps.
• The approach offers a trade-off between artifact reduction and a slight increase in acquisition time.