Positron Emission Tomography Image Reconstruction with Time-of-Flight Data Using a SPLAT System Model

Abstract

Abstract This thesis presents the development and evaluation of a physics-based system model tailored for image reconstruction in a novel time-of-flight (TOF) positron emission tomography (PET) brain scanner. The scanner design features a continuous, multilayered monolithic thin detector, offering improved spatial resolution and sensitivity compared to conventional PET systems. To fully leverage these hardware advances, a custom system model called the SPLAT model was implemented. The SPLAT system model integrates key resolution loss mechanisms, including non-collinearity, detector blur, and coincidence timing resolution (CTR), using a Gaussian framework. In addition, positron range and photon attenuation, both implemented in a form viable for 3D, were incorporated to enable more realistic reconstruction. A simulation tool for PET list-mode (LM) data was developed based on this model, enabling controlled evaluation of reconstruction performance. Two simulation strategies were implemented to discretize the continuous detector domain. The forward and backprojection operators were implemented with performance optimizations in C and integrated into an iterative maximum-likelihood expectation-maximization (ML-EM) reconstruction pipeline. Image quality was quantitatively assessed using normalized RMSE metrics, with analyses explicitly linking reconstruction accuracy to characteristics of the modeled point spread function.