Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking.

Area of Science:

• Medical Imaging
• Neuroimaging
• Biophysics

Background:

• Motion artifacts significantly degrade diffusion-weighted (DW) brain MRI quality.
• Accurate reconstruction is crucial for diagnosing neurological conditions, particularly in vulnerable populations like neonates and children.

Purpose of the Study:

• To develop and validate a novel motion-robust approach for DW brain MRI reconstruction.
• To improve the accuracy and reliability of DW imaging analysis in the presence of head motion.

Main Methods:

• Employs slice-to-volume registration to track temporal head motion during DW MRI acquisition.
• Utilizes an outlier-robust Kalman filter coupled with slice-to-volume registration for motion parameter estimation.
• Corrects diffusion gradient directions and estimates diffusion tensors from motion-corrected data using weighted linear least squares.

Main Results:

• Demonstrated significant improvements in DW MRI reconstruction quality compared to traditional volume-to-volume registration methods.
• Successfully evaluated in adult volunteers with induced motion and clinical DW imaging of neonates and children.
• Showcased enhanced ability to retrieve diagnostic information from motion-corrupted DW imaging data.

Conclusions:

• The proposed slice-to-volume registration and Kalman filtering approach effectively corrects for head motion in DW brain MRI.
• This method offers a valuable tool for improving the quality and utility of DW imaging, especially in pediatric and motion-sensitive populations.
• Facilitates more reliable analysis of brain microstructure from otherwise compromised DW imaging datasets.