Resolution recovery reconstruction for a Compton camera

Abstract

The spatial resolution from Compton cameras suffers from measurement uncertainties in interaction positions and energies. The degree of degradation in spatial resolution is shift-variant (SV) over the field-of-view (FOV) because the imaging principle is based on the conical surface integration. In our study, the shift-variant point spread function (SV-PSF) is derived from point source measurements at various positions in the FOV and is incorporated into the system matrix of a fully three-dimensional, accelerated reconstruction, i.e. the listmode ordered subset expectation maximization (LMOSEM) algorithm, for resolution recovery. Simulation data from point sources were used to estimate SV and asymmetric parameters for Gaussian, Cauchy, and general parametric PSFs. Although little difference in the fitness accuracy between Gaussian and general parametric PSFs was observed, the general parametric model showed greater flexibility over the FOV in shaping the curve between that for Gaussian and Cauchy functions. The estimated asymmetric SV-PSFs were incorporated into the LMOSEM for resolution recovery. For simulation data from a single point source at the origin, all LMOSEM-SV-PSFs improved the spatial resolution by 2.6 times over the standard LMOSEM. For two point-source simulations, reconstructions also gave a two-fold improvement in spatial resolution and resulted in a greater recovered activity ratio at different positions in the FOV.