FULLY 3D BAYESIAN IMAGE-RECONSTRUCTION FOR THE ECAT EXACT HR+

A fully 3D Bayesian method is described for high resolution reconstruc tion of images from the Siemens/CTI ECAT EXACT HR+ whole body positron emission tomography (PET) scanner. To maximize resolution recovery fr om the system we model depth dependent geometric efficiency, intrinsic detector efficiency, photon pair non-colinearity, crystal penetration and inter-crystal scatter. We also explicitly model the effects of ax ial rebinning and angular mashing on the detection probability or syst em matrix. By fully exploiting sinogram symmetries and using a factore d system matrix and automated indexing schemes, we are able to achieve substantial savings in both the storage size and time required to com pute forward and backward projections. Reconstruction times are furthe r reduced using multi-threaded programming on a four processor Unix se rver. Bayesian reconstructions are computed using a Huber prior and a shifted-Poisson likelihood model that accounts for the effects of rand oms subtraction and scatter. Reconstructions of phantom data show that the 3D Bayesian method can achieve improved FWHM resolution and contr ast recovery ratios at matched background noise levels compared to bot h the 3D reprojection method and an OSEM method based on the shifted-P oisson model.