Rodríguez Pérez Sunay, Coolen Johan, Marshall Nicholas W, Cockmartin Lesley, Biebaû Charlotte, Desmet Jeroen, De Wever Walter, Struelens Lara, Bosmans Hilde
KU Leuven, Medical Physics and Quality Assessment, Leuven, Belgium.
SCK CEN, Radiation Protection Dosimetry and Calibration, Mol, Belgium.
J Med Imaging (Bellingham). 2021 Jan;8(Suppl 1):013501. doi: 10.1117/1.JMI.8.S1.013501. Epub 2021 Jan 4.
We describe the creation of computational models of lung pathologies indicative of COVID-19 disease. The models are intended for use in virtual clinical trials (VCT) for task-specific optimization of chest x-ray (CXR) imaging. Images of COVID-19 patients confirmed by computed tomography were used to segment areas of increased attenuation in the lungs, all compatible with ground glass opacities and consolidations. Using a modeling methodology, the segmented pathologies were converted to polygonal meshes and adapted to fit the lungs of a previously developed polygonal mesh thorax phantom. The models were then voxelized with a resolution of and used as input in a simulation framework to generate radiographic images. Primary projections were generated via ray tracing while the Monte Carlo transport code was used for the scattered radiation. Realistic sharpness and noise characteristics were also simulated, followed by clinical image processing. Example images generated at 120 kVp were used for the validation of the models in a reader study. Additionally, images were uploaded to an Artificial Intelligence (AI) software for the detection of COVID-19. Nine models of COVID-19 associated pathologies were created, covering a range of disease severity. The realism of the models was confirmed by experienced radiologists and by dedicated AI software. A methodology has been developed for the rapid generation of realistic 3D models of a large range of COVID-19 pathologies. The modeling framework can be used as the basis for VCTs for testing detection and triaging of COVID-19 suspected cases.
我们描述了用于指示新冠病毒疾病的肺部病变计算模型的创建过程。这些模型旨在用于虚拟临床试验（VCT），以针对胸部X光（CXR）成像进行特定任务的优化。通过计算机断层扫描确诊的新冠患者的图像被用于分割肺部衰减增加的区域，所有这些区域均与磨玻璃影和实变相符。使用一种建模方法，将分割出的病变转换为多边形网格，并进行调整以适配先前开发的多边形网格胸部模型的肺部。然后将模型以 的分辨率进行体素化，并用作模拟框架的输入以生成射线照相图像。通过光线追踪生成主要投影，同时使用蒙特卡罗传输代码计算散射辐射。还模拟了逼真的清晰度和噪声特征，随后进行临床图像处理。在120 kVp下生成的示例图像用于在阅片者研究中验证模型。此外，图像被上传到人工智能（AI）软件以检测新冠病毒。创建了九个与新冠相关病变的模型，涵盖了一系列疾病严重程度。模型的逼真度得到了经验丰富的放射科医生和专用AI软件的证实。已经开发出一种方法，可快速生成一系列新冠病变的逼真三维模型。该建模框架可作为虚拟临床试验的基础，用于检测和分类新冠疑似病例。
