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

To develop a comprehensive, hardware free, and real-time motion tracking method for prospective correction of motion artifacts in EPI-based brain MRI, which remains highly sensitive to rapid motion and geometric distortion.

We propose a gradient-induced voltage-based motion tracking framework that leverages time-varying magnetic flux detected by small surface coils to estimate six degrees of freedom of rigid-body head motion. An analytical equation describing gradient-induced voltage was derived from electromagnetic theory and validated through second-order modeling and high fidelity numerical simulations. A lightweight nonlinear regression model maps induced voltages to estimates of head pose. Simulation-based analyses evaluated sensitivity to coil placement variability, gradient noise, and non-ideal trajectories.

The proposed forward model demonstrated stability under moderate coil misalignment and realistic noise conditions without requiring sequence modification or additional scanner hardware. In free-running EPI experiments with human subjects, the predicted motion estimates correlated strongly with image-based measurements, supporting the real-time feasibility of the method. In human experiments, predicted motion closely matched SPM8 estimates, with mean deviations ≈ 1mm in translation and ≈ 1° in rotation across all six degrees of freedom, even for large movements (≈ 20° rotation and any translations within the gradient linearity range).

The framework offers accurate and computationally efficient head motion estimation, making it suitable for integration into prospective motion correction pipelines.

This work advances MRI motion correction by enabling rapid, scanner hardware modification-free, and sequence independent tracking using only gradient-induced coil signals, thereby enhancing the reliability of neuroimaging under naturalistic motion conditions.