Modeling observer performance in low-dose CT assessments

项目总结/摘要 X射线计算机断层扫描（CT）已经成为许多医疗领域诊断成像的支柱。 由于其能够以高精度呈现身体的内部结构，因此被称为医学。这 导致在美国CT成像的使用大幅增加。因此， 持续关注CT成像中的剂量减少。然而，证明有效的剂量减少是 挑战性根据定义，这些技术寻求保留诊断质量， 影响诊断性能。使用受试者工作特征（ROC）的临床阅片师研究 方法学是评价诊断性能效果的公认标准。但这些 研究是昂贵和耗时的，识别小的影响需要大量的 读者与案例这导致了美国剂量减少声明的“模型观察员”的发展 美国食品药品监督管理局（FDA）。目前，FDA至少有三项剂量减少声明使用了 模型观察者研究，以证实其使用迭代重建降低CT剂量的声明 算法 这个项目的基础是我们认识到这些模型的有效性相对较低， 考虑到人类视觉系统和被评估的图像的复杂性。我们提出了一个深入的表征人类观察员的反应，在CT剂量减少相关的任务。目的 这项研究的目的是开发和验证一个（或多个）观测器性能模型， CT剂量降低的图像重建评估。要使模型观察器在这一领域发挥作用， 必须接受作为一个合理的预测人类观察者的表现为一定范围内的相关 任务这激发了我们的总体方法，以及我们研究计划的许多细节。 我们的计划是收集一组初始的心理物理数据，使用这些数据来开发我们的模型， 然后从各种剂量的模拟中预测CT重建的性能。然后我们收集 这些图像上的心理物理数据，以量化预测的准确性，并将其与 其他型号。具体目标1涉及在具有噪声统计的任务中收集心理物理数据 类似于CT剂量评估。具体目标2旨在通过拟合来开发任务绩效模型 模型参数的几个候选模型的数据从目标1。具体目标3提出 根据来自具有以下特征的图像的一组新心理物理数据对观察者表现进行前瞻性预测 使用现代迭代方法重建。在项目期结束时，我们预计 更好地理解观察者如何执行困难的定位和区分任务， 噪声CT图像，以及这个过程如何受到与图像相关的剂量的影响。

Project Summary/Abstract X-ray computed tomography (CT) has become a mainstay of diagnostic imaging in many areas of medicine because of its ability to render internal structures of the body with high accuracy. This has resulted in a substantial increase in the use of CT imaging in the United States. As a result, there has been sustained interest in dose reduction in CT imaging. However, demonstrating effective dose reduction is challenging. By definition, such techniques seek to retain diagnostic quality with little or no measureable effect on diagnostic performance. Clinical reader studies using receiver operating characteristic (ROC) methodology are the accepted standard for evaluating diagnostic performance effects. However, these studies are expensive and time consuming, and identifying small effects requires prohibitively large sets of readers and cases. This has led to the development of “model-observers” for dose reduction claims at the US Food and Drug Administration (FDA). At this time, at least three dose reduction claims at FDA have used model observer studies to substantiate their claim of CT dose reduction using iterative reconstruction algorithms. The basis for this project is our recognition that such models have had relatively little validation, given the complexity of both the human visual system and the images being evaluated. We propose an in-depth characterization of human observer responses in tasks related to dose reduction in CT. The purpose of this research is to develop and validate a model (or models) of observer performance for use in assessments of image reconstruction for CT dose reduction. For a model observer to be of use in this area, it must be accepted as a reasonable predictor of human-observer performance for some range of relevant tasks. This motivates our general approach, and many specifics of our research plan. Our plan is to collect an initial set of psychophysical data, use this data to develop our model, and then predict performance in CT reconstructions from simulations at a variety of doses. We then collect the psychophysical data on these images to quantify predictive accuracy and to compare it to the accuracy of other models. Specific Aim 1 involves the collection of psychophysical data in tasks with noise statistics similar to CT dose assessments. Specific Aim 2 seeks to develop models of task performance by fitting model parameter for several candidate models to the data from Aim 1. Specific Aim 3 proposes a prospective prediction of observer performance in a new set of psychophysical data from images that have been reconstructed using modern iterative methods. At the conclusion of the project period, we expect to have a better understanding of how observers perform difficult localization and discrimination tasks in noisy CT images, and how this process in influenced by the dose associated with images.