HIGH-RESOLUTION 3D BAYESIAN IMAGE-RECONSTRUCTION USING THE MICROPET SMALL-ANIMAL SCANNER

A Bayesian method is described for reconstruction of high-resolution 3 D images from the microPET small-animal scanner. Resolution recovery i s achieved by explicitly modelling the depth dependent geometric sensi tivity for each voxel in combination with an accurate detector respons e model that includes factors due to photon pair non-collinearity and inter-crystal scatter and penetration. To reduce storage and computati onal costs we use a factored matrix in which the detector response is modelled using a sinogram blurring kernel. Maximum a posteriori (MAP) images are reconstructed using this model in combination with a Poisso n likelihood function and a Gibbs prior on the image. Reconstructions obtained from point source data using the accurate system model demons trate a potential for near-isotropic FWHM resolution of approximately 1.2 mm at the center of the held of view compared with approximately 2 mm when using an analytic 3D reprojection (3DRP) method with a ramp f ilter. These results also show the ability of the accurate system mode l to compensate for resolution loss due to crystal penetration produci ng nearly constant radial FWHM resolution of 1 mm out to a 4 mm radius . Studies with a point source in a uniform cylinder indicate that as t he resolution of the image is reduced to control noise propagation the resolution obtained using the accurate system model is superior to th at obtained using 3DRP at matched background noise levels. Additional studies using pie phantoms with hot and cold cylinders of diameter 1-2 .5 mm and (18)FDG animal studies appear to confirm this observation.