Tracking of rigid head motion during MRI using an EEG system

Area of Science:

• Neuroimaging
• Biomedical Engineering
• Signal Processing

Background:

• Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fEEG-fMRI) are powerful tools for studying brain activity.
• Gradient switching in MRI induces signal distortions in EEG, which are dependent on head position and orientation.
• Accurate tracking of head movements is crucial for reliable fEEG-fMRI data analysis.

Purpose of the Study:

• To present CapTrack, a novel method for tracking head movements during MRI using existing EEG hardware.
• To demonstrate the capability of CapTrack to record signals induced by native imaging gradients for head motion estimation.
• To enable retrospective correction of head motion without prolonging MRI scan times.

Main Methods:

• Characterize MRI sequences and develop a fast calibration sequence using phantom scanning.
• Establish a linear relationship between induced EEG signals and head pose variations.
• Utilize recorded induced signals during target sequence scanning for motion tracking and correction.

Main Results:

• Achieved head-pose tracking at 27.5 Hz during echo planar imaging (EPI) with high accuracy compared to rigid body alignment.
• Demonstrated retrospective correction of 3D gradient-echo imaging, showing improved average edge strength.
• Reported high similarity between CapTrack estimates and interleaved navigator estimates for head motion.

Conclusions:

• Head motion can be estimated from gradient switching recordings with minimal sequence modification.
• CapTrack offers real-time tracking at low computational cost, synchronized with image acquisition.
• The method leverages readily available EEG equipment in research institutions, making it broadly applicable.