Pragmatic image reconstruction for the MiCES Fully-3D mouse imaging PET scanner

Abstract

We present a pragmatic approach to image reconstruction for data from the MiCES fully-3D mouse imaging PET scanner under construction at the University of Washington. Our approach is modeled on fully-3D image reconstruction used in clinical PET scanners, which is based on Fourier rebinning (FORE) followed by 2D iterative image reconstruction. The use of iterative methods allows modeling the effects of statistical noise and attenuation etc., while FORE accelerates the reconstruction process by reducing the fully-3D data to a stacked set of independent 2D sinograms. Preliminary investigations have indicated that nonstationary detector point-spread response effects, which are ignored for clinical imaging, significantly impact image quality for the MiCES scanner geometry. To model the effect of nonstationary detector point spread response, we have added a factorized system matrix to the ASPIRE reconstruction library. The current implementation uses FORE+AWOSEM followed by postreconstruction 3D Gaussian smoothing. The results indicate that the proposed approach produces a dramatic improvement in resolution without undue increases in noise.