Free-Running EPI Motion Tracking in MRI via Gradient-Coupled Coil Signal.

Area of Science:

• Magnetic Resonance Imaging (MRI)
• Neuroimaging
• Biomedical Engineering

Background:

• EPI-based brain MRI is highly sensitive to motion artifacts and geometric distortion.
• Existing motion correction methods often require additional hardware or sequence modifications.

Purpose of the Study:

• To develop a hardware-free, real-time motion tracking method for prospective correction of motion artifacts in EPI-based brain MRI.
• To enhance the reliability of neuroimaging under naturalistic motion conditions.

Main Methods:

• A gradient-induced voltage-based framework was developed, leveraging surface coils to detect magnetic flux.
• An analytical equation derived from electromagnetic theory estimated six degrees of freedom of rigid-body head motion.
• A nonlinear regression model mapped induced voltages to head pose, with simulations evaluating sensitivity to various factors.

Main Results:

• The method demonstrated stability under coil misalignment and noise without scanner hardware modification.
• Predicted motion estimates in free-running EPI experiments strongly correlated with image-based measurements.
• Human experiments showed predicted motion closely matched SPM8 estimates, with mean deviations of ≈ 1mm/1° for large movements.

Conclusions:

• The framework provides accurate, computationally efficient head motion estimation for prospective motion correction pipelines.
• This advancement enables rapid, sequence-independent tracking using only gradient-induced coil signals.
• The technique enhances neuroimaging reliability in the presence of naturalistic head motion.