Design of a motion-compensation OSEM list-mode algorithm for resolution-recovery reconstruction for the HRRT

Abstract

The HRRT PET system has the potential to produce human brain images with resolution better than 3 mm. To achieve the best possible accuracy and precision, we have designed MOLAR, a motion-compensation OSEM list-mode algorithm for resolution-recovery reconstruction on a computer cluster with the following features: direct use of list mode data with dynamic motion information (Polaris); exact reprojection of each line-of- response (LOR); system matrix computed from voxel-to-LOR distances (radial and axial); spatially varying resolution model implemented for each event by selection from precomputed line spread functions based on factors including detector obliqueness, crystal layer, and block detector position; distribution of events to processors and to subsets based on order of arrival; removal of voxels and events outside a reduced field-of-view defined by the attenuation map; no pre-corrections to Poisson data, i.e., all physical effects are defined in the model; randoms estimation from singles; model-based scatter simulation incorporated into the iterations; and component-based normalization. Preliminary computation estimates suggest that reconstruction of a single frame in one hour is achievable. Careful evaluation of this system will define which factors play an important role in producing high resolution, low-noise images with quantitative accuracy.