Modeling observer performance in low-dose CT assessments

项目摘要/摘要 X射线计算机体层摄影术(CT)已成为许多领域的诊断成像的支柱 医学，因为它有能力高精度地描绘身体内部结构。这有 导致CT成像在美国的使用大幅增加。因此，已经有了 对CT成像中剂量减少的持续兴趣。然而，证明有效的剂量减少是 很有挑战性。根据定义，这样的技术寻求在很少或没有可测量的情况下保持诊断质量 对诊断性能的影响。使用受试者工作特性(ROC)的临床读者研究 方法学是评估诊断性能效果的公认标准。然而，这些 研究既昂贵又耗时，而且识别微小的影响需要令人望而却步的大集合 读者和案例。这导致了在美国为减少剂量声称而发展的“模范观察员” 美国食品和药物管理局(FDA)。目前，FDA至少有三项减量声明使用了 模型观察者的研究证实了他们使用迭代重建减少CT剂量的说法 算法。 这个项目的基础是我们认识到这样的模型得到的验证相对较少， 考虑到人类视觉系统和被评估图像的复杂性。我们提出了人类观察者在与CT剂量减少相关的任务中的反应的深入表征。目的 这项研究的目的是开发和验证观察者性能的模型，以用于 图像重建对减少CT剂量的评估。对于一个在这一领域有用的模型观察者来说，它 必须被接受为人-观察者在一定范围内的合理预测 任务。这激励了我们的总体方法，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，以及 然后从不同剂量的模拟中预测CT重建的性能。然后我们收集 这些图像上的心理物理数据来量化预测的准确性，并将其与 其他型号。具体目标1涉及收集具有噪声统计的任务中的心理物理数据 类似于CT剂量评估。具体目标2寻求通过拟合来开发任务绩效的模型 来自目标1的数据的几个候选模型的模型参数。具体目标3提出 在一组新的心理物理数据中对观察者表现的预期预测 是用现代迭代方法重建的。在项目期结束时，我们预计 更好地了解观察员如何执行困难的本地化和歧视任务 噪声CT图像，以及这一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.