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: 9901529
• 负责人: Cynthia H McCollough
• 金额: $ 55.48万
• 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 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9901529
• 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; evidence base; imaging facilities; imaging modality; improved; individual patient; innovation; learning strategy; low dose computed tomography; practice setting; programs; radiologist; reconstruction; skills; stem; success; tool

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.

项目摘要/摘要 计算机断层扫描(CT)是一种很好的诊断工具，但它会使患者暴露在电离辐射中。 因此，国际上进行了一项密集的努力，以降低用于CT的辐射剂量水平 成像。我们的长期目标是开发和验证高度可翻译的方法，这些方法可以量化 对于任何指定的诊断任务，确定提供所需诊断准确性的CT方案 最低患者剂量。根据设计，这些方法将适用于任何型号的扫描仪或成像实践。 在我们的第一个竞争获奖期间，我们展示了扫描仪和扫描方案的差异 (例如，剂量、重建算法)可能会导致诊断性能的显著差异。更多 重要的是，我们的多读者、多案例观察者研究显示， 读者(放射科医生)和不同的病例。这些变化大于因剂量而引起的变化。 因此，迫切需要量化和减少这些巨大的性能差异，但几乎没有什么工作 在这个主题上已经做过了。只有在满足这一关键需求之后，CT社区才能实现一致的 广泛的扫描仪、案例和读取器的诊断性能水平，因此可以安全地采用 腹部成像中的低剂量--这是最常见的CT应用之一。因此，我们现在有第二个 长期目标，即减少因病例和疾病而导致的诊断性能差异 阅读器的变化，即使使用适当的CT方案，特别是在较低的剂量。 此续订申请的具体目标是：1)验证我们建立最低- 剂量协议(针对目标性能水平)确实适用于任何型号的扫描仪；2) 描述导致低诊断性能的病例、病变和读卡器因素 可接受的扫描协议；以及3)开发自适应评估和学习策略，以提高读者的 跨病例和病变类型的诊断技能。我们将通过三个具体目标实现这些目标： 1.针对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性能的病例、病变和读者预测因子。 3.开发适应性学习和评估技术，以应对案例和读者的差异。 拟议的工作意义重大，因为它将使用客观和定量的衡量标准，以及领导- 边缘教育和自适应学习技术，以提高低诊断性能和一致性 剂量CT成像。这项工作具有创新性，因为这是第一次，病例、病变和读者特征 导致诊断性能下降的特征将被表征，然后通过最先进的技术缓解 适应性评估和培训技术。这项工作的结果将允许任何成像设施优化 它们的剂量水平不会影响从CT获得的挽救生命的诊断信息。