Review of GPU-based Monte Carlo simulation platforms for transmission and emission tomography in medicine

Objectives. Monte Carlo (MC) simulation remains the gold standard for modeling complex physical interactions in transmission and emission tomography, with graphic processing unit (GPU) parallel computing offering unmatched computational performance and enabling practical, large-scale MC applications. In recent years, rapid advancements in both GPU technologies and tomography techniques have been observed. Harnessing emerging GPU capabilities to accelerate MC simulation and strengthen its role in supporting the rapid growth of medical tomography has become an important topic. To provide useful insights, we conducted a comprehensive review of state-of-the-art GPU-accelerated MC simulations in tomography, highlighting current achievements and underdeveloped areas.Approach. We reviewed key technical developments across major tomography modalities, including computed tomography (CT), cone-beam CT (CBCT), positron emission tomography (PET), single-photon emission CT, proton CT , emerging techniques, and hybrid modalities. We examined MC simulation methods and major CPU-based MC platforms that have historically supported medical imaging development, followed by a review of GPU acceleration strategies, hardware evolutions, and leading GPU-based MC simulation packages. Future development directions were also discussed.Main results. Significant advancements have been achieved in both tomography and MC simulation technologies over the past half-century. The introduction of GPUs has enabled speedups often exceeding 100-1000 times over CPU implementations, providing essential support to the development of new imaging systems. Emerging GPU features like ray-tracing cores, tensor cores, and GPU-execution-friendly transport methods offer further opportunities for performance enhancement.Significance. GPU-based MC simulation is expected to remain essential in advancing medical emission and transmission tomography. With the emergence of new concepts such as training machine learning with synthetic data, digital twins for healthcare, and virtual clinical trials, improving hardware portability and modularizing GPU-based MC codes to adapt to these evolving simulation needs represent important directions for future research. This review aims to provide useful insights for researchers, developers, and practitioners in the relevant fields.