Characterization of USC-MAP Image Reconstruction on MicroPET-R4

We present a quantitative evaluation of a Maximum a Posteriori image reconstruction (USC-MAP) implemented on the Concorde microPET-R4 camera. This iterative reconstruction algorithm uses a Bayesian reconstruction technique for the reconstruction of PET images which includes an accurate modeling of the camera response, the Poisson distribution of coincidence data, and the physics of positron decay. The spatial resolution measurements were made by using a small cylindrical chamber containing a thin plastic tube filled with concentrated /sup 18/F-FDG, scanned at different radial positions in the field of view. A specially designed miniature rat heart phantom and a cylindrical chamber containing four different size spheres were used to measure recovery coefficients mimicking cardiac and small lesion quantitative applications. The images were reconstructed with FORE+2D-FBP, 2D-OSEM, 3DRP, and USC-MAP. The experiments show that substantial gain in spatial resolution is achieved using this novel image reconstruction algorithm. In particular, we showed that the spatial resolution degradation at large radial offsets due to the depth of interaction is in large part corrected with USC-MAP, a consequence of the accurate system detection modeling. The gain in resolution results in recovery of activity concentration in hot spheres is achieved with USC-MAP as compared to FORE-2D-FBP. The recovery coefficient increases by 9% in spheres with diameter of 1.24 cm and 62% in spheres with diameter of 0.39 cm in the experiment with /sup 18/F-FDG. More significant improvement is observed in the case of long positron range isotope such as /sup 76/Br.