Retrospective Rigid Motion Correction in k-Space for Segmented Radial MRI

Area of Science:

• Medical Imaging
• Biophysics
• Computer Vision

Background:

• Motion during magnetic resonance imaging (MRI) acquisition significantly degrades image quality.
• Existing self-navigation techniques often require a separate motion-free reference scan.
• Accurate motion estimation is crucial for artifact reduction in dynamic MRI.

Purpose of the Study:

• To develop a novel self-navigation technique for motion correction in MRI.
• To enable motion parameter estimation directly from acquired radial data in k-space.
• To improve image quality in dynamic MRI scans affected by continuous motion.

Main Methods:

• Utilized inherent correlations between radial segments in k-space for motion parameter derivation.
• Employed the Phase Correlation Method for registration exclusively within k-space.
• Validated the technique on 2-D dynamic brain scans with continuous motion from six volunteers.
• Investigated the benefits of a bit-reversed ordering scheme for retrospective motion correction.

Main Results:

• Achieved robust and accurate rigid motion registration using as few as 32 radial profiles.
• Demonstrated significant image quality improvement with retrospective motion correction compared to conventional sliding window methods.
• Successfully performed self-navigation on dynamic brain scans corrupted by continuous motion.
• Showcased the advantages of the bit-reversed ordering scheme for radial trajectories.

Conclusions:

• The proposed k-space based self-navigation method provides a fast and efficient means for measuring rigid motion.
• This technique effectively reduces motion artifacts in dynamic MRI without requiring a separate reference scan.
• The method offers a significant advancement in motion correction for real-time MRI applications.