Accuracy and Precision in CT Quantification of COPD Through Virtual Imaging Trials

慢性阻塞性肺疾病（COPD）是导致死亡的主要原因。患病率增加，COPD 这对患者和供应商来说是一个沉重的负担。计算机断层扫描（CT）可以提供有价值的信息 关于疾病的结构和功能异常，正如许多研究所证明的那样， 使用定量CT来表征和评估治疗。例如，COPDGene研究 最近显示了定量CT在重新定义COPD诊断中的重要作用， 评估肺气肿随时间的进展。然而，这些生物标志物在不同的 扫描仪、设置和患者属性。迫切需要通过优化和 协调CT图像，以便在当前和新兴扫描仪中进行可靠的生物标志物定量。 通过使用物理体模或患者图像的常规方法，该目标是不可能的。物理 体模通常过于简化，不能代表COPD的复杂解剖和生理 患者患者图像是地面实况有限的，即，患者的确切解剖结构和生理结构 全知。此外，基于患者的比较需要在不同时间对相同受试者进行多次采集。 不同的扫描仪和设置。这在伦理上是不可能的，因为重复成像会增加吸收的 辐射剂量这些挑战可以通过使用虚拟成像试验（VITs）来克服， 使用患者和扫描仪的计算模型在计算机中进行研究。VITs可以提供可靠的 为COPD成像的挑战提供了一个实用的解决方案，提供了患者和扫描仪的逼真模型。 目前在COPD的背景下缺乏这样的模型。 我们开发并利用逼真的虚拟成像工具集来系统地评估和优化CT 在扫描仪、成像参数和患者属性中，COPD患者的生物标志物。我们开发的 第一个具有不同属性和严重程度的真实COPD患者模型库。再加上准确的 模型的不同扫描仪，幻影将被用来生成集地面实况已知的虚拟CT 案例，传播给研究界并用于系统地评估 当前和新兴的扫描仪、各种患者属性以及图像处理算法的影响 （通过全国挑战），对COPD生物标志物的准确性和精确性。此外，我们开发和 优化用于COPD量化的基于事实的人工智能算法。我们优化 算法的准确性和再现性，利用地面真实已知的模拟图像 .我们 然后协调不同扫描仪的CT设置，以准确评估COPD成像 单时间点和纵向研究的生物标志物。 将对前两个图像进行研究 处理算法，在挑战中确定，以及我们开发的算法。通过这些努力， 该项目将把CT定位为一种更可靠的方法，用于改善COPD的表征和监测。

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of death. Increasing in prevalence, COPD is a major burden to patients and providers. Computed tomography (CT) can provide valuable information about the structural and functional abnormalities of the disease as demonstrated in numerous studies where quantitative CT is deployed to characterize and evaluate the treatment. For instance, the COPDGene study has recently shown the substantial role of quantitative CT in the redefinition of COPD diagnosis, and in evaluating the progression of emphysema over time. However, these biomarkers vary across different scanners, settings, and patient attributes. There is a crucial need to manage this variability by optimizing and harmonizing CT images for reliable biomarker quantifications across both current and emerging scanners. This goal is not possible through conventional methods of using physical phantoms or patient images. Physical phantoms are often oversimplified and not representative of the complex anatomy and physiology of COPD patients. Patient images are ground-truth-limited, i.e., the exact anatomy and physiology of the patient is not fully known. Further, patient-based comparisons require multiple acquisitions of the same subjects across different scanners and settings. This is not ethically possible since repeated imaging increases the absorbed radiation dose. These challenges can be overcome through the use of virtual imaging trials (VITs) where studies are performed in silico using computational models of patients and scanners. VITs can provide reliable and practical solution to the challenge of COPD imaging provided realistic models of patients and scanners. Such models are currently lacking in the context of COPD. We develop and then utilize realistic virtual imaging toolsets to systematically evaluate and optimize CT biomarkers in COPD patients across scanners, imaging parameters, and patient attributes. We develop the first library of realistic COPD patient models with diverse attributes and severities. Coupled with accurate models of different scanners, the phantoms will be used to generate sets of ground-truth-known virtual CT cases, to be disseminated to the research community and to be used to systematically evaluate the effects of current and emerging scanners, various patient attributes, and the effects of image processing algorithms (through a national challenge), on the accuracy and precision of COPD biomarkers. Further, we develop and optimize a truth-based artificial intelligence-based algorithm for COPD quantifications. We optimize the algorithm for accuracy and reproducibility, taking advantage of the ground-truth known simulated images . We then harmonize CT settings across different scanners to accurately and precisely assess COPD imaging biomarkers for both single time-point and longitudinal studies. The studies will be done for the top two image processing algorithms, identified in the challenge, as well as our developed algorithm. Through these efforts, the project will position CT as a more reliable method for improved characterization and monitoring of COPD.