Virtual Imaging Trials for Coronavirus Disease (COVID-19).
Virtual Imaging Trials for Coronavirus Disease (COVID-19).
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冠状病毒病（COVID-19）的虚拟成像试验。
DOI：
10.2214/ajr.20.23429
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Samei E
中科院分区：
文献类型：
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作者：
Abadi E;Paul Segars W;Chalian H;Samei E
虚拟成像试验是一个独特的框架，通过使用具有代表性的患者计算模型和经过验证的成像模拟器模拟成像实验，可以极大地促进成像方法的评估和优化。本研究的目的是展示虚拟成像试验如何适用于冠状病毒病（COVID-19）的成像研究，从而有效评估和优化CT和x线摄影采集和分析工具，以实现COVID-19的可靠成像和管理。我们开发了第一个COVID-19患者的计算模型，并作为原理证明，展示了如何将它们与成像模拟器结合起来进行COVID-19成像研究。对于模型的身体习性，我们使用了杜克大学开发的4D扩展心脏-躯干（XCAT）模型。从确诊的20例COVID-19患者的CT图像中分割出COVID-19异常的形态学特征，并纳入XCAT模型。在给定的疾病区域内，XCAT对肺实质的质地和材料进行了修改，以匹配临床图像中观察到的特性。为了展示其实用性，使用扫描仪专用CT和放射成像模拟器对三个已开发的COVID-19计算幻影进行了虚拟成像。主观上，模拟的异常在形状和纹理上都是真实的。结果表明，5、25、50 mas图像的异常区噪比分别为1.6、3.0、3.6。本研究开发的工具集为使用虚拟成像试验有效评估和优化CT和放射成像采集和分析工具提供了基础，以帮助管理COVID-19大流行。
The virtual imaging trial is a unique framework that can greatly facilitate the assessment and optimization of imaging methods by emulating the imaging experiment using representative computational models of patients and validated imaging simulators. The purpose of this study was to show how virtual imaging trials can be adapted for imaging studies of coronavirus disease (COVID-19), enabling effective assessment and optimization of CT and radiography acquisitions and analysis tools for reliable imaging and management of COVID-19. We developed the first computational models of patients with COVID-19 and as a proof of principle showed how they can be combined with imaging simulators for COVID-19 imaging studies. For the body habitus of the models, we used the 4D extended cardiac-torso (XCAT) model that was developed at Duke University. The morphologic features of COVID-19 abnormalities were segmented from 20 CT images of patients who had been confirmed to have COVID-19 and incorporated into XCAT models. Within a given disease area, the texture and material of the lung parenchyma in the XCAT were modified to match the properties observed in the clinical images. To show the utility, three developed COVID-19 computational phantoms were virtually imaged using a scanner-specific CT and radiography simulator. Subjectively, the simulated abnormalities were realistic in terms of shape and texture. Results showed that the contrast-to-noise ratios in the abnormal regions were 1.6, 3.0, and 3.6 for 5-, 25-, and 50-mAs images, respectively. The developed toolsets in this study provide the foundation for use of virtual imaging trials in effective assessment and optimization of CT and radiography acquisitions and analysis tools to help manage the COVID-19 pandemic.