Trade-offs in human observer performance, image quality metrics, and patient dose

人类观察者表现、图像质量指标和患者剂量的权衡

• 批准号: 10322422
• 负责人: Cynthia H McCollough
• 金额: $ 55.32万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10322422
• 关键词: Abdomen; Address; Adopted; Affect; Algorithms; Award; Classification; Clinical; Communities; Computed Tomography Scanners; Detection; Diagnostic; Diagnostic Sensitivity; Dose; Education; Equipment; Goals; Hospitals; Human; Image; International; Ionizing radiation; Lead; Learning; Lesion; Link; Liver; Liver diseases; Long-Term Effects; Manufacturer Name; Metastatic Neoplasm to the Liver; Methods; Modeling; Noise; Patient-Focused Outcomes; Patients; Performance; Protocols documentation; Public Health; Radiation Dose Unit; Reader; Research; Resources; Scanning; Sensitivity and Specificity; Specific qualifier value; System; Techniques; Technology; Time; Training Technics; Translating; Variant; Work; X-Ray Computed Tomography; abdominal CT; adaptive learning; base; cost effective; design; diagnostic accuracy; diagnostic tool; evidence base; imaging facilities; imaging modality; improved; individual patient; innovation; learning strategy; low dose computed tomography; practice setting; programs; radiologist; reconstruction; skills; stem; success

PROJECT SUMMARY/ABSTRACT Computed tomography (CT) is an excellent diagnostic tool, but it exposes patients to ionizing radiation. Consequently, an intensive, international effort has been made to reduce the radiation dose levels used for CT imaging. Our long-term objective is to develop and validate highly translatable methods that can quantitatively determine, for any specified diagnostic task, CT protocols that deliver the needed diagnostic accuracy at the lowest patient dose. These methods will be, by design, applicable to any scanner model or imaging practice. In our first competitive award period, we demonstrated that differences in scanners and scanning protocols (e.g. doses, reconstruction algorithms) can lead to substantial variations in diagnostic performance. More importantly, our multi-reader, multi-case observer studies demonstrated wide variations in performance among readers (radiologists) and across different cases. These variations were larger than the variations due to dose. Thus, a critical need exists to quantify and reduce these large variations in performance, but little work has been done on this topic. Only after addressing this critical need can the CT community achieve a consistent level of diagnostic performance over a wide range of scanners, cases, and readers and therefore safely adopt lower doses in abdominal imaging – one of the most common CT applications. Thus, we now have a second long-term objective, which is to reduce the variation in diagnostic performance that occurs due to case and reader variation, even when appropriate CT protocols are used, and especially at lower doses. The specific goals of this renewal application are to 1) validate that our methods for establishing lowest- dose protocols (for a targeted level of performance) are indeed applicable to any scanner make or model; 2) characterize case, lesion, and reader factors that lead to low diagnostic performance despite an otherwise acceptable scan protocol; and 3) develop adaptive assessment and learning strategies to improve readers' diagnostic skills across case and lesion type. We will accomplish these goals through three specific aims: 1. For multiple scanner models and protocols, demonstrate the success of our protocol optimization engine. 2. For abdominal CT, determine case, lesion, and reader predictors of radiologist diagnostic performance. 3. Develop adaptive learning and assessment techniques to address case and reader variability. The proposed work is significant because it will use objective and quantitative metrics, as well as leading- edge education and adaptive learning technology, to improve diagnostic performance and consistency in low- dose CT imaging. This work is innovative because, for the first time, the case, lesion and reader features leading to decreased diagnostic performance will be characterized and then mitigated with state-of-the-art adaptive assessment and training techniques. The results of this work will allow any imaging facility to optimize their dose levels without compromising the lifesaving diagnostic information obtained from CT.

项目概要/摘要 计算机断层扫描 (CT) 是一种出色的诊断工具，但它会使患者暴露在电离辐射下。 经过检查，国际上已做出大量努力来降低 CT 所用的辐射剂量水平 我们的长期目标是开发并高度验证可定量的可翻译方法。 对于任何指定的诊断任务，确定能够在特定时间提供所需诊断准确性的 CT 协议 根据设计，这些方法将适用于任何扫描仪型号或成像实践。 在我们的第一个竞争性颁奖期间，我们证明了扫描仪和扫描协议的差异 （例如剂量、重建算法）可能会导致诊断性能的显着变化。 重要的是，我们的多读者、多案例观察者研究表明，不同读者之间的表现存在很大差异。 读者（放射科医生）和不同病例之间的差异大于剂量引起的差异。 因此，迫切需要量化和减少这些巨大的性能变化，但几乎没有开展任何工作。 只有在解决了这一关键需求之后，CT 社区才能达成一致。 各种扫描仪、案例和阅读器的诊断性能水平，因此可以安全地采用 腹部成像中的较低剂量——最常见的 CT 应用之一因此，我们现在有了第二种。 长期目标，即减少因病例和情况而发生的诊断性能变化 即使使用适当的 CT 方案，尤其是在较低剂量下，读取器也会发生变化。 此更新申请的具体目标是 1) 验证我们的最低建立方法 剂量协议（针对目标性能水平）确实适用于任何扫描仪品牌或型号；2) 描述导致诊断性能低下的病例、病变和读者因素，尽管在其他方面 可接受的扫描协议；3) 制定适应性评估和学习策略以提高读者的能力 我们将通过三个具体目标来实现这些目标： 1. 对于多种扫描仪型号和协议，展示我们的协议优化引擎的成功。 2. 对于腹部 CT，确定放射科医生诊断表现的病例、病变和读者预测因素。 3. 开发适应性学习和评估技术来解决案例和读者的可变性。 拟议的工作意义重大，因为它将使用客观和定量的指标，以及领先的- 边缘教育和自适应学习技术，以提高低诊断性能和一致性 这项工作具有创新性，因为它首次体现了病例、病变和读卡器的特点。 导致诊断性能下降的问题将被表征，然后通过最先进的技术来缓解 这项工作的结果将使任何成像设施得以优化。 其剂量水平，而不影响从 CT 获得的救生诊断信息。