Mammography and breast tomosynthesis simulator for virtual clinical trials
Mammography and breast tomosynthesis simulator for virtual clinical trials
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DOI：
10.1016/j.cpc.2020.107779
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发表时间：
2021-04-01
影响因子：
6.3
通讯作者：
Badano, Aldo
中科院分区：
文献类型：
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作者：
Badal, Andreu;Sharma, Diksha;Badano, Aldo
计算机建模和模拟越来越多地被用于预测硅胶中X射线成像设备的临床性能，并生成用于训练和测试机器学习算法的合成患者图像。我们详细描述了在开源的GPU加速的蒙特卡罗X射线成像模拟程序MC-GPU中实现的计算模型。该代码最初是为了模拟射线照相和计算机断层扫描而开发的，现已扩展为复制商业全场数字乳房X光照相和数字乳房断层扫描(DBT)设备。该代码最近被用来对3000个虚拟乳房模型进行成像，目的是在Silico中复制一项临床试验，该临床试验用于支持监管机构批准DBT作为乳腺癌筛查的乳房X光照相的替代品。更新的代码实现了更真实的X射线源模型(扩展的3D焦点、断层合成获取轨迹、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、电子噪声和防散射网格的直接转换硒探测器)。该软件使用高分辨率体素化几何模型来表示乳房解剖。为了减少对GPU内存的要求，代码将体素存储在二叉树结构的内存中。二叉树是一种有效的压缩机制，因为具有相同成分的许多体素被组合在公共树枝中，同时保持在任何位置对幻象成分的随机访问。一种不需要计算光线-体素界面的增量散射光线跟踪算法被用来最小化存储器访问。报告了旨在确定所实施的计算模型的可信度的多个软件验证和确认步骤。软件验证是使用数字质量控制体模和理想针孔相机完成的。验证是通过复制临床实践中使用的标准台架测试实验并与实验测量进行比较来进行的。一项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损伤和数学观察者的电子临床试验流水线进行的。我们表明，MC-GPU能够模拟包含临床图像中许多可变性来源的X射线投影，并且模拟结果对输入参数中的一些不确定性是稳健的。讨论了所实现的计算模型的局限性。爱思唯尔出版公司(Elsevier B.V.)
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. Published by Elsevier B.V.