Depth-of-interaction (DOI) encoding is an effective strategy for reducing parallax error and preserving spatial resolution in positron emission tomography (PET), particularly in compact small-animal scanners. To enable efficient simulation-driven design of DOI-capable systems, we extend the GPU-accelerated Monte Carlo toolkit gPET to support flexible multi-layer detector geometries. The original three-level hierarchical detector model in gPET (panel-module-crystal) was expanded by introducing an intermediate "layer" level, enabling parameterized modeling of stacked scintillator architectures. The photon transport algorithm was correspondingly updated to sample interactions across multiple layers and detector panels while preserving GPU-efficient memory usage. The framework was validated using three scanner configurations: a conventional single-layer ring (H2RSPET-1CL), an aligned split-layer design (H2RSPET-1CL-split), and an offset dual-layer design (H2RSPET-2CL). System performance was evaluated following NEMA NU4-2008 protocols using sensitivity, spatial resolution, and Derenzo phantom simulations with CASToR-based maximum likelihood expectation maximization reconstruction. The H2RSPET-1CL and H2RSPET-1CL-split configurations produced statistically identical hit distributions, while H2RSPET-2CL exhibited the expected offset interaction patterns. Sensitivity of H2RSPET-2CL remained comparable to H2RSPET-1CL, generally within about 2-5 percent, while radial spatial resolution improved substantially (0.8-1.6 mm vs. 1.0-4.2 mm from the center to a 50 mm radial offset). Runtime performance remained essentially unchanged between configurations. The extended gPET framework therefore enables fast and flexible simulation of multi-layer PET detectors and supports efficient optimization of DOI-enabled PET system designs.
原文链接
分享
引用
