Abstract Computer modeling and simulations are increasingly being used to predict the clinical performance of x-ray imaging devices in silico, and to generate synthetic patient images for training and testing of machine learning algorithms. We present a detailed description of the computational models implemented in the open source GPU-accelerated Monte Carlo x-ray imaging simulation code MC-GPU. This code, originally developed to simulate radiography and computed tomography, has been extended to replicate a commercial full-field digital mammography and digital breast tomosynthesis (DBT) device. The code was recently used to image 3000 virtual breast models with the aim of reproducing in silico a clinical trial used in support of the regulatory approval of DBT as a replacement of mammography for breast cancer screening. The updated code implements a more realistic x-ray source model (extended 3D focal spot, tomosynthesis acquisition trajectory, tube motion blurring) and an improved detector model (direct-conversion Selenium detector with depth-of-interaction effects, fluorescence tracking, electronic noise and anti-scatter grid). The software uses a high resolution voxelized geometry model to represent the breast anatomy. To reduce the GPU memory requirements, the code stores the voxels in memory within a binary tree structure. The binary tree is an efficient compression mechanism because many voxels with the same composition are combined in common tree branches while preserving random access to the phantom composition at any location. A delta scattering ray-tracing algorithm which does not require computing ray-voxel interfaces is used to minimize memory access. Multiple software verification and validation steps intended to establish the credibility of the implemented computational models are reported. The software verification was done using a digital quality control phantom and an ideal pinhole camera. The validation was performed reproducing standard bench testing experiments used in clinical practice and comparing with experimental measurements. A sensitivity study intended to assess the robustness of the simulated results to variations in some of the input parameters was performed using an in silico clinical trial pipeline with simulated lesions and mathematical observers. We show that MC-GPU is able to simulate x-ray projections that incorporate many of the sources of variability found in clinical images, and that the simulated results are robust to some uncertainty in the input parameters. Limitations of the implemented computational models are discussed. Program summary Program title: MCGPU_VICTRE CPC Libary link to program files: http://dx.doi.org/10.17632/k5x2bsf27m.1 Licensing provisions: CC0 1.0 Programming language: C (with NVIDIA CUDA extensions) Nature of problem: The health risks associated with ionizing radiation impose a limit to the amount of clinical testing that can be done with x-ray imaging devices. In addition, radiation dose cannot be directly measured inside the body. For these reasons, a computational replica of an x-ray imaging device that simulates radiographic images of synthetic anatomical phantoms is of great value for device evaluation. The simulated radiographs and dosimetric estimates can be used for system design and optimization, task-based evaluation of image quality, machine learning software training, and in silico imaging trials. Solution method: Computational models of a mammography x-ray source and detector have been implemented. X-ray transport through matter is simulated using Monte Carlo methods customized for parallel execution in multiple Graphics Processing Units. The input patient anatomy is represented by voxels, which are efficiently stored in the video memory using a new binary tree structure compression mechanism.

中文翻译：

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用于虚拟临床试验的乳房 X 线照相术和乳房断层合成模拟器

摘要 计算机建模和模拟越来越多地用于预测 X 射线成像设备的临床性能，并生成合成的患者图像以用于机器学习算法的训练和测试。我们详细描述了在开源 GPU 加速蒙特卡罗 X 射线成像仿真代码 MC-GPU 中实现的计算模型。该代码最初开发用于模拟放射成像和计算机断层扫描，现已扩展到复制商业全场数字乳房 X 光检查和数字乳房断层合成 (DBT) 设备。该代码最近被用于对 3000 个虚拟乳房模型进行成像，目的是在计算机上复制一项临床试验，该试验用于支持 DBT 的监管批准，作为乳腺癌筛查的乳房 X 线照相术的替代品。更新后的代码实现了更逼真的 X 射线源模型（扩展的 3D 焦斑、断层合成采集轨迹、管运动模糊）和改进的探测器模型（具有相互作用深度效应、荧光跟踪、电子噪声的直接转换硒探测器）和防散射网格）。该软件使用高分辨率体素化几何模型来表示乳房解剖结构。为了减少 GPU 内存需求，代码将体素存储在内存中的二叉树结构中。二叉树是一种有效的压缩机制，因为许多具有相同组成的体素组合在公共树枝中，同时保留对任何位置的幻影组成的随机访问。不需要计算光线-体素接口的增量散射光线追踪算法用于最小化内存访问。报告了旨在建立实施的计算模型的可信度的多个软件验证和验证步骤。软件验证是使用数字质量控制模型和理想的针孔相机完成的。验证是通过重现临床实践中使用的标准台架测试实验并与实验测量值进行比较而进行的。旨在评估模拟结果对某些输入参数变化的稳健性的敏感性研究是使用具有模拟病变和数学观察者的计算机临床试验管道进行的。我们表明 MC-GPU 能够模拟 X 射线投影，这些投影结合了临床图像中发现的许多可变性来源，并且模拟结果对输入参数中的某些不确定性具有鲁棒性。讨论了所实现的计算模型的局限性。程序摘要 程序名称：MCGPU_VICTRE CPC 库程序文件链接：http://dx.doi.org/10.17632/k5x2bsf27m.1 许可条款：CC0 1.0 编程语言：C（带有 NVIDIA CUDA 扩展） 问题性质：健康风险与电离辐射相关的技术限制了可以使用 X 射线成像设备进行的临床测试的数量。此外，辐射剂量不能直接在体内测量。由于这些原因，模拟合成解剖体模的射线照相图像的 X 射线成像设备的计算副本对于设备评估具有重要价值。模拟射线照片和剂量估计可用于系统设计和优化、基于任务的图像质量评估、机器学习软件培训和计算机成像试验。求解方法：已经实现了乳腺 X 射线源和检测器的计算模型。使用为在多个图形处理单元中并行执行而定制的蒙特卡罗方法来模拟 X 射线在物质中的传输。输入的患者解剖结构由体素表示，体素使用新的二叉树结构压缩机制有效地存储在视频存储器中。