Herraiz Joaquin L, Lopez-Montes Alejandro, Badal Andreu
Complutense University of Madrid, EMFTEL, Grupo de Física Nuclear and IPARCOS, Madrid, 28040, Spain.
Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdiSSC), Madrid,28040, Spain.
Comput Phys Commun. 2024 Mar;296. doi: 10.1016/j.cpc.2023.109008. Epub 2023 Nov 13.
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 seconds in one GPU. MCGPU-PET provides an estimation of true and scatter coincidences and spurious background (for positron-gamma emitters such as I) 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.
蒙特卡罗(MC)模拟常用于正电子发射断层扫描(PET)中对辐射的发射、传输和/或探测进行建模。在这项工作中,我们引入了一种用于PET模拟的新型开源MC软件MCGPU-PET,其设计目的是充分利用现代图形处理器(GPU)的计算能力,以模拟从体素化源和物质分布中每秒获取超过1亿次符合事件。这个新的模拟器是基于PENELOPE的MCGPU代码的扩展,该代码先前用于锥束CT和乳腺摄影应用。我们通过将加速后的代码与GATE和PeneloPET模拟进行比较,验证了其准确性,结果显示两者的一致性约在10%以内。作为该代码用于快速估计PET符合事件的一个示例应用,我们模拟了NEMA IQ体模的扫描。在一个GPU中,54秒内生成了一个包含63.82亿次真符合事件和7.31亿次散射符合事件的全3D正弦图。MCGPU-PET对真符合事件、散射符合事件和伪背景(对于像碘这样的正电子-γ发射体)的估计速度比基于CPU的MC模拟器快3个数量级。这种显著的加速使得该代码能够在迭代图像重建过程中用于准确的散射和瞬发γ背景估计。