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.

Purpose: We suggest a motion correction concept that employs free-induction-decay (FID) navigator signals to continuously monitor motion and to guide the acquisition of image navigators for prospective motion correction following motion detection. Methods: Motion causes out-of-range signal changes in FID time series that, and in this approach, initiate the acquisition of an image navigator. Co-registration of the image navigator to a reference provides rigid-body-motion parameters to facilitate prospective motion correction. Both FID and image navigator are integrated into a prototype magnetization-prepared rapid gradient-echo (MPRAGE) sequence. The performance of the method is investigated using image quality metrics and the consistency of brain volume measurements. Results: Ten healthy subjects were scanned (a) while performing head movements (nodding, shaking, and moving in zdirection) and (b) to assess the co-registration performance. Mean absolute errors of 0.27 6 0.38 mm and 0.19 6 0.24 for translation and rotation parameters were measured. Image quality was qualitatively improved after correction. Significant improvements were observed in automated image quality measures and for most quantitative brain volume computations after correction. Conclusion:The presented method provides high sensitivity to detect head motion while minimizing the time invested in acquiring navigator images. Limits of this implementation arise from temporal resolution to detect motion, false-positive alarms, and registration accuracy. Magn Reson