Motion compensation and pose measurement uncertainty in awake small animal positron emission tomography using stochastic origin ensembles

Abstract

In order to remove the influence of anaesthetic agents on neurological function and to expand the range of imaging tasks available it is preferable to image small animals in an awake state. Accurate activity estimates in awake small animal Positron Emission Tomography rely on the measurement of animal head pose over the time-course of the scan. Pose measurements are then incorporated into the emission data during image reconstruction, compensating for animal motion. Uncertainty in pose measurement can impact reconstructed image quality by effectively degrading scanner resolution, and hence that of the reconstructed image. In small animal imaging, regions of interest can be small in comparison to image-space voxelisation so that the precision of estimates taken from a single reconstructed image can be difficult to gauge. Stochastic Origin Ensembles provides a means of estimating a more complete statistical description of the emission data than other methods of image reconstruction. In this investigation, rigid motion compensation is incorporated into the Stochastic Origin Ensembles algorithm and explored using simulated data. Realistic motion is modeled within a GATE simulation and measurement uncertainty is incorporated into the pose data using both simulated perturbations as well as experimental trials using a motion tracking system. Sampling from the posterior distribution is conducted using the Stochastic Origin Ensembles algorithm and compared to image reconstruction using the Maximum Likelihood-Expectation Maximisation algorithm. Using Stochastic Origin Ensembles both regional and single voxel parameters were investigated. The impact of varying levels of pose measurement uncertainty on image-space parameters was demonstrated and assessed.