Fully 3D PET Iterative Reconstruction Using Distance-Driven Projectors and Native Scanner Geometry

Incorporating all data corrections into the system model optimizes image quality in statistical iterative PET image reconstruction. We have previously shown that including attenuation, randoms and scatter in the forward 3D iterative model results in faster convergence and improved image quality for ML-OSEM. This paper extends this work to allow the accurate modeling of crystal efficiency, detector deadtime, and the native block-based detector geometry. In order to model these effects, it is necessary to perform forward and back-projections directly from image space to the projection geometry of the PET scanner, rather than to an idealized, equally spaced projection space. We have modified the distance-driven projectors to accurately model both the uneven spacing of the sinogram due to the ring curvature as well as the gaps resulting from the block structure of the scanner. This results in a reconstruction method, which can incorporate the crystal efficiency and block deadtime effects into the forward system model while maintaining the fast reconstruction times enabled by the distance driven projector design. Results on the GE Discovery STEtrade scanner show improvements in image resolution consistent with removing the interpolative smoothing of the data into the equally spaced projection space.