MCGPU-PET: An open-source real-time Monte Carlo PET simulator

Monte Carlo (MC) simulations are commonly used to model the emission, transmission, and/or detection of radiation in Positron Emission Tomography (PET). In this work, we introduce a new open-source MC software for PET simulation, MCGPU-PET, which has been designed to fully exploit the computing capabilities of modern GPUs to simulate the acquisition of more than 100 million coincidences per second from voxelized sources and material distributions. The new simulator is an extension of the PENELOPE-based MCGPU code previously used in cone-beam CT and mammography applications. We validated the accuracy of the accelerated code by comparing it to GATE and PeneloPET simulations achieving an agreement within 10 percent approximately. As an example application of the code for fast estimation of PET coincidences, a scan of the NEMA IQ phantom was simulated. A fully 3D sinogram with 6382 million true coincidences and 731 million scatter coincidences was generated in 54 s in one GPU. MCGPU-PET provides an estimation of true and scatter coincidences and spurious background (for positron-gamma emitters such as 124I) at a rate 3 orders of magnitude faster than CPU-based MC simulators. This significant speed-up enables the use of the code for accurate scatter and prompt-gamma background estimations within an iterative image reconstruction process. Program Summary: Program Title: MCGPU-PET Licensing provisions: CC0 1.0 Programming language: C (with NVIDIA CUDA extensions) Journal reference of the previous version: Badal A, Sharma D, Graff C G, Zeng R and Badano A, Mammography and breast tomosynthesis simulator for virtual clinical trials, Computer Physics Communications 261, 107,779 (2021); https://doi.org/10.1016/j.cpc.2020.107779 Reasons for the new version: The open source x-ray imaging simulator MCGPU was modified to simulate Positron Emission Tomography (PET), extending the medical imaging applications that can be modeled with the system. The main radiation transport algorithm has not been changed, but new radiation source and detector models were implemented. Nature of problem: Due to their stochastic nature, MC methods require a large number of computations to obtain low-noise estimations, which makes them usually very time-consuming. This has limited their application in PET imaging to offline tasks where the simulation time is not a critical factor, leaving out other relevant cases that would benefit from accurate calculations. Solution method: The development of computers with multiple CPUs and GPUs for scientific computing has enabled tackling real-time MC simulations, i.e. performing simulations fast enough to obtain results at the same time as other processes of the imaging pipeline. We introduce a new open source software, MCGPU-PET, for simulating PET acquisitions with GPUs that simulates more than 100 million detected coincidences per second, which makes it even faster than many real PET acquisitions. The software samples gamma rays emitted from inside a voxelized anatomical model following a user input radiotracer distribution. The gamma ray coincidences arriving at the ideal ring detector are tallied in either a sinogram or list mode, and can be reconstructed using standard PET reconstruction algorithms.