Optimizing a Feature-Based Motion Tracking System for Prospective Head Motion Estimation in MRI and PET/MRI

Abstract

Patient head motion during magnetic resonance imaging (MRI) of the brain causes artifacts that limit diagnostic value. Prospective motion correction can, in principle, allow artifact-free imaging for any MRI sequence. Optical motion tracking systems are effective at measuring head motion for prospective motion correction but normally rely on attached markers, which are cumbersome and can move relative to the head. In this study, we optimized and benchmarked an optical motion tracking system that uses in-bore stereo-optical cameras to track feature patches on the forehead rather than attached markers. Feature patches were applied to the forehead in the form of an easily removable ink stamp or temporary tattoo. This approach has flexibility in camera positioning and allows tracking of a variety of surfaces on the human face, even those lacking prominent contours. This is advantageous given the limited space and line-of-sight with typical head coils. We optimized the accuracy and efficiency of two key components of the method: 1) the geometry of the stereo camera setup and 2) the feature detection and matching algorithm used for pose estimation. The absolute accuracy of the optimized feature-based method was compared with a commercial marker-based motion tracking system. Estimates of head motion of 15 volunteers simultaneously tracked by the two systems resulted in average positional discrepancies of less than 3 mm using a stamp and submillimeter average discrepancy using a feature-dense tattoo. These results suggest the feasibility of obtaining accurate head motion data for prospective motion compensated MRI using optimized feature-based motion tracking.