Robust retrospective motion correction of head motion using navigator-based and markerless motion tracking techniques

Area of Science:

• Medical Imaging
• Neuroimaging
• Magnetic Resonance Imaging

Background:

• Head motion during brain MRI acquisition introduces artifacts that degrade image quality.
• Accurate motion estimation and correction are crucial for reliable neuroimaging analysis.

Purpose of the Study:

• To investigate artifacts from various head motions in brain MRI.
• To evaluate the effectiveness of retrospective correction using two motion-tracking techniques.

Main Methods:

• MPRAGE images acquired on a 3T scanner from nine healthy participants.
• Simulated head motions: circular, stepwise, nodding, and slow diagonal.
• Motion estimation using 3D fat-navigator (FatNavs) and a markerless Tracoline device.
• Retrospective image reconstruction based on motion estimates.

Main Results:

• High image quality was recovered for stepwise and slow diagonal motions, comparable to no-motion scans.
• FatNav-based correction improved for stepwise motion with skull masking.
• Circular motion resulted in severe artifacts that were not fully compensated by either technique.

Conclusions:

• Retrospective motion correction is effective for stepwise and slow diagonal head motions in brain MRI.
• Circular motion remains a challenge for current retrospective correction methods.
• Motion-tracking techniques show promise but require further refinement for complex motion patterns.