Uniform Spatial resolution list mode reconstruction for the HRRT

Abstract

In expectation-maximization (EM) reconstruction, uniform spatial resolution in the field of view (FOV) is difficult to achieve as the iterations are typically stopped prematurely. Maximum-a-posterior (MAP) reconstruction is known to accelerate convergence by applying desirable constraints on the estimated parameters. However, object-dependent spatial resolution is still present using traditional penalty functions, even when the algorithm fully converges. The goal of this study is to test a uniform-resolution MAP (UR-MAP) algorithm for static PET reconstruction, as an extension of the work of Fessler et at and our list-mode OSEM algorithm (MOLAR), for the HRRT. The local spatial resolution, characterized by local impulse response (LIR) function is adjusted by applying spatially varying smoothing to the image based on local counts. A simulation study shows that standard MAP reconstruction with a global smoothing parameter over-smoothed high-activity regions and reduced the contrast between hot regions and the background. With the global smoothing parameter P=O.Ol, the contrast recovery coefficient (CRC) reaches 87% in low-background areas and 77% in high background areas. UR-MAP reduced the effective smoothing in high-background areas, and thus produced a uniform CRC of 87%, independent of background activity and local contrast. Preliminary results in human brain reconstructions show that UR-MAP yields images with reduced smoothing in high-activity regions than the standard MAP method.