Résumé
Purpose: To develop a novel framework for rapid, intrinsic head motion measurement in MRI using FID navigators (FIDnavs) from a multichannel head coil array.
Methods: FIDnavs encode substantial rigid-body motion information; however, current implementations require patient-specific training with external tracking data to extract quantitative positional changes. In this work, a forward model of FIDnav signals was calibrated using simulated movement of a reference image within a model of the spatial coil sensitivities. A FIDnav module was inserted into a nonselective 3D FLASH sequence, and rigid-body motion parameters were retrospectively estimated every readout time using nonlinear optimization to solve the inverse problem posed by the measured FIDnavs. This approach was tested in simulated data and in 7 volunteers, scanned at 3T with a 32-channel head coil array, performing a series of directed motion paradigms.
Results: FIDnav motion estimates achieved mean absolute errors of 0.34 +/- 0.49 mm and 0.52 +/- 0.61 degrees across all subjects and scans, relative to ground-truth motion measurements provided by an electromagnetic tracking system. Retrospective correction with FIDnav motion estimates resulted in substantial improvements in quantitative image quality metrics across all scans with intentional head motion.
Conclusions: Quantitative rigid-body motion information can be effectively estimated using the proposed FIDnav-based approach, which represents a practical method for retrospective motion compensation in less cooperative patient populations.
Methods: FIDnavs encode substantial rigid-body motion information; however, current implementations require patient-specific training with external tracking data to extract quantitative positional changes. In this work, a forward model of FIDnav signals was calibrated using simulated movement of a reference image within a model of the spatial coil sensitivities. A FIDnav module was inserted into a nonselective 3D FLASH sequence, and rigid-body motion parameters were retrospectively estimated every readout time using nonlinear optimization to solve the inverse problem posed by the measured FIDnavs. This approach was tested in simulated data and in 7 volunteers, scanned at 3T with a 32-channel head coil array, performing a series of directed motion paradigms.
Results: FIDnav motion estimates achieved mean absolute errors of 0.34 +/- 0.49 mm and 0.52 +/- 0.61 degrees across all subjects and scans, relative to ground-truth motion measurements provided by an electromagnetic tracking system. Retrospective correction with FIDnav motion estimates resulted in substantial improvements in quantitative image quality metrics across all scans with intentional head motion.
Conclusions: Quantitative rigid-body motion information can be effectively estimated using the proposed FIDnav-based approach, which represents a practical method for retrospective motion compensation in less cooperative patient populations.