Markerless motion estimation for motion-compensated clinical brain imaging
Motion-compensated brain imaging can dramatically reduce the artifacts and quantitative degradation
associated with voluntary and involuntary subject head motion during positron emission tomography (PET),
single photon emission computed tomography (SPECT) and computed tomography (CT). However, motion-compensated
imaging protocols are not in widespread clinical use for these modalities. A key reason for this seems to be
the lack of a practical motion tracking technology that allows for smooth and reliable integration of
motion-compensated imaging protocols in the clinical setting. We seek to address this problem by investigating
the feasibility of a highly versatile optical motion tracking method for PET, SPECT and CT geometries. The
method requires no attached markers, relying exclusively on the detection and matching of distinctive facial
features. We studied the accuracy of this method in 16 volunteers in a mock imaging scenario by comparing the
estimated motion with an accurate marker-based method used in applications such as image guided surgery. A
range of techniques to optimize performance of the method were also studied. Our results show that the markerless
motion tracking method is highly accurate (<2?mm discrepancy against a benchmarking system) on an ethnically
diverse range of subjects and, moreover, exhibits lower jitter and estimation of motion over a greater range
than some marker-based methods. Our optimization tests indicate that the basic pose estimation algorithm is very
robust but generally benefits from rudimentary background masking. Further marginal gains in accuracy can be
achieved by accounting for non-rigid motion of features. Efficiency gains can be achieved by capping the number
of features used for pose estimation provided that these features adequately sample the range of head motion
encountered in the study. These proof-of-principle data suggest that markerless motion tracking is amenable to
motion-compensated brain imaging and holds good promise for a practical implementation in clinical PET, SPECT
and CT systems.