A virtual clinical trial using projection-based nodule insertion to determine radiologist reader performance in lung cancer screening CT.
A virtual clinical trial using projection-based nodule insertion to determine radiologist reader performance in lung cancer screening CT.
复制标题
一项虚拟临床试验，使用基于投影的结节插入来确定放射科医生阅片员在肺癌筛查 CT 中的表现。
DOI：
10.1117/12.2255593
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
McCollough,CynthiaH
中科院分区：
文献类型：
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作者：
Yu,Lifeng;Hu,Qiyuan;Koo,ChiWan;Takahashi,EdwinA;Levin,DavidL;Johnson,TuckerF;Hora,MeganJ;Dirks,Shane;Chen,Baiyu;McMillan,Kyle;Leng,Shuai;Fletcher,JG;McCollough,CynthiaH
使用模型观察者的基于任务的图像质量评估有望为CT剂量优化提供一种有效、定量和客观的方法。在这种方法可以可靠地应用于实践之前，需要建立它与放射科医生在同一临床任务中的表现的相关性。然而，确定人类观察者在明确定义的临床任务中的表现一直是一个挑战，因为收集大量阳性病例需要付出巨大的努力。为了克服这一挑战，我们开发了一种基于精确投影的插入技术。在这项研究中，我们提供了一个使用该工具和一个低剂量模拟工具的虚拟临床试验，以确定放射科医生在检测肺结节方面的表现，作为辐射剂量、结节类型、结节大小和重建方法的函数。病变插入和低剂量模拟工具一起被证明提供了灵活性，在明确定义的条件下生成逼真的临床病例。在此虚拟临床试验中获得的读取器性能数据可以作为开发肺结节检测的模型观察者以及肺癌筛查CT剂量和方案优化的基础。
Task-based image quality assessment using model observers is promising to provide an efficient, quantitative, and objective approach to CT dose optimization. Before this approach can be reliably used in practice, its correlation with radiologist performance for the same clinical task needs to be established. Determining human observer performance for a well-defined clinical task, however, has always been a challenge due to the tremendous amount of efforts needed to collect a large number of positive cases. To overcome this challenge, we developed an accurate projection-based insertion technique. In this study, we present a virtual clinical trial using this tool and a low-dose simulation tool to determine radiologist performance on lung-nodule detection as a function of radiation dose, nodule type, nodule size, and reconstruction methods. The lesion insertion and low-dose simulation tools together were demonstrated to provide flexibility to generate realistically-appearing clinical cases under well-defined conditions. The reader performance data obtained in this virtual clinical trial can be used as the basis to develop model observers for lung nodule detection, as well as for dose and protocol optimization in lung cancer screening CT.