Towards Optimal Collimator Design for the PEDRO Hybrid Imaging System

摘要

The Pixelated Emission Detector for RadiOisotopes (PEDRO) is a hybrid imaging system designed for the measurement of single photon emission from small animal models. The proof-of-principle device consists of a Compton-camera situated behind a mechanical collimator and is intended to provide optimal detection characteristics over a broad spectral range, from 30 to 511 keV. An automated routine has been developed for the optimization of large-area slits in the outer regions of a collimator which has a central region allocated for pinholes. The optimization was tested with a GEANT4 model of the experimental prototype. The data were blurred with the expected position and energy resolution parameters and a Bayesian interaction ordering algorithm was applied. Images were reconstructed using cone back-projection. The results show that the optimization technique allows the large-area slits to both sample fully and extend the primary field of view (FoV) determined by the pinholes. The slits were found to provide truncation of the back-projected cones of response and also an increase in the success rate of the interaction ordering algorithm. These factors resulted in an increase in the contrast and signal-to-noise ratio of the reconstructed image estimates. Of the two configurations tested, the cylindrical geometry outperformed the square geometry, primarily because of a decrease in artifacts. This was due to isotropic modulation of the cone surfaces, that can be achieved with a circular shape. Also, the cylindrical geometry provided increased sampling of the FoV due to more optimal positioning of the slits. The use of the cylindrical collimator and application of the transmission function in the reconstruction was found to improve the resolution of the system by a factor of 20, as compared to the uncollimated Compton camera. Although this system is designed for small animal imaging, the technique can be applied to any application of single photon imaging.