IEEE transactions on bio-medical engineering · 2026
Free-Running EPI Motion Tracking in MRI via Gradient-Coupled Coil Signal.
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Bhuiyan Enamul H, Mahi Muntafa M, Navin Mushifikur R, Chowdhury Muhammad E H
Аннотация
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSION: The framework offers accurate and computationally efficient head motion estimation, making it suitable for integration into prospective motion correction pipelines.
SIGNIFICANCE: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSION: The framework offers accurate and computationally efficient head motion estimation, making it suitable for integration into prospective motion correction pipelines.
SIGNIFICANCE: 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.