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

项目概要/摘要 计算机断层扫描（CT）是一种很好的诊断工具，但它使患者暴露于电离辐射。 因此，国际上已经做出了密集的努力来降低用于CT的辐射剂量水平 显像我们的长期目标是开发和验证高度可翻译的方法， 针对任何指定的诊断任务，确定CT协议，该协议提供所需的诊断准确性， 最低患者剂量。通过设计，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奖项期间，我们证明了扫描仪和扫描协议的差异 (e.g.剂量、重建算法）可能导致诊断性能的显著变化。更 重要的是，我们的多读者，多病例观察者研究表明， 读者（放射科医生）和不同的情况下。这些变化大于剂量引起的变化。 因此，迫切需要量化和减少这些大的性能变化，但很少有工作 在这个话题上做过。只有在解决了这一关键需求之后，CT社区才能实现一致的 在广泛的扫描仪、病例和阅读器上的诊断性能水平，因此可以安全地采用 腹部成像中的低剂量-最常见的CT应用之一。因此，我们现在有第二个 长期目标，即减少因病例而发生的诊断性能变化， 即使使用适当的CT方案，并且特别是在较低剂量下，也会出现读数器变化。 本更新申请的具体目标是：1）验证我们建立最低- 剂量协议（针对目标性能水平）确实适用于任何扫描仪品牌或型号; 2） 表征导致诊断性能低下的病例、病变和阅片师因素，尽管 可接受的扫描协议;和3）发展适应性评估和学习策略，以提高读者的阅读能力 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.对于多种扫描仪型号和协议，演示我们的协议优化引擎的成功。 2.对于腹部CT，确定放射科医师诊断性能的病例、病变和阅片师预测因素。 3.开发适应性学习和评估技术，以解决案例和读者的变化。 拟议的工作是重要的，因为它将使用客观和量化的指标，以及领先的- 边缘教育和自适应学习技术，以提高低水平的诊断性能和一致性 剂量CT成像。这项工作是创新的，因为，第一次，案件，病变和读者的特点， 将对导致诊断性能下降的因素进行表征，然后使用最先进的技术进行缓解 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， 他们的剂量水平，而不损害从CT获得的救命诊断信息。

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.