Image reconstruction using tetrahedral voxels: A list mode implementation for awake animal imaging

Abstract

Reliable interpretation of results from pre-clinical Emission Tomography studies is often hampered by the requirement that the animal be anesthetised, affecting certain neuro-transmission systems and cerebral blood flow. Animal tracking and motion compensation techniques have been exploited to account for that rigid motion associated with head movement - allowing brain imaging in small animals. However, rigid motion cannot be assumed for extra cranial activity. Tetrahedral mesh approaches have been applied in cardiac reconstruction to account for non-rigid motion in clinical environments. In this investigation a list-mode approach to calculation of the elements of the system matrix using a tetrahedral image space is developed and evaluated by applying rigid motion compensation to both simulated and experimental data. Experimental data for which only rigid motion was applied were used to demonstrate the application of variable voxel-size over the image space. A simple small animal model comprising rigid (head) and non-rigid (body) motion was developed and the resulting voxelised sources were simulated using GATE. Motion compensation based on rigid transforms allowed targeted voxel sampling and demonstrates the impact of the non-rigid motion of activity estimates. The tetrahedral mesh should allow future investigations to extend correction to also include non-rigid estimates of small animal motion.