NSF/FDA SIR: Modeling Observer Performance in CT Dose Reduction Assessments

PI：Abbey，Craig K.Proposal：1445737标题：在CT剂量减少评估中的建模观察者性能意义CT成像的减少已成为医学成像中的一个重要优先事项，这是随着这种成像方式的使用增加而出现的。在过去的15年里，大型综合医疗系统中CT扫描的估计年率增加了大约三倍，令人担忧的是，诱发癌症的估计高达2%。这推动了对减少剂量的密集研究，包括减少剂量成像协议，该协议使用计算机密集重建技术来恢复本来可能丢失的信息，以减少这些协议产生的额外噪声。最近，赞助商要求FDA批准有关减少剂量方案的声明，并预计在不久的将来会看到更多这样的申请。然而，证明有效的剂量减少是具有挑战性的。根据定义，这种技术寻求在对诊断性能几乎或没有可测量的影响的情况下保持诊断质量。使用受试者操作特征(ROC)方法的临床读者研究是评估诊断效果的公认标准。然而，这些研究非常昂贵和耗时，而且识别微小的影响或非劣势，需要大量的读者和案例，令人望而却步。因此，该领域越来越多地转向模型观察者，作为标准观察者表现研究的替代方案。为了有效地预测人类观察者的表现，模型观察者需要在心理物理研究中用人类数据进行广泛的验证。该项目的目标是在低剂量X射线CT成像的背景下进行验证。该项目的目的是收集大量与CT剂量减少相关的心理物理数据，并使用从视觉科学转化来的分类图像方法来分析这些数据，以更好地了解人类观察者如何在这些噪声图像中执行任务。技术说明人类观察者如何根据噪声和可能失真的图像执行任务仍然是一个悬而未决的问题。拟议的调查将提供观察员的业绩数据和一种对人类观察员进行建模的方法，远远超出迄今所做的工作。将分类图像方法与检测和定位任务一起使用，他们将有机会模拟人类观察者如何利用噪声CT图像中的信息，以及图像信息的使用如何随着剂量的减少和/或剂量减少方法(平滑等)的变化而变化。都是适用的。因此，这一结果将引起更广泛的视觉科学界以及医学图像感知和CT图像重建领域的兴趣。我们的研究结果将允许使用更好的观察者模型来评估剂量减少。通过提供一个更易于处理的观察者模型研究，该研究可以在实验室环境中进行，而不需要临床阅读器和病例，寻求新的CT成像技术的研究人员将更愿意在他们的研究中评估和优化剂量。预计这将导致更广泛地使用基于模型的方法来微调图像剂量程序。人们还预计，这一结果将对视觉科学产生影响，建立更有洞察力的模型，研究人类观察者如何在噪声存在的情况下执行视觉任务。

PI: Abbey, Craig K.Proposal: 1445737Title: Modeling Observer Performance in CT Dose Reduction AssessmentsSignificanceDose reduction in CT imaging has become an important priority in medical imaging that has emerged with the increased use of this imaging modality. The estimated annual rate of CT scanning in large integrated healthcare systems has increased roughly three-fold in the last 15 years, with worrisome estimates of induced cancers as high as 2%. This has motivated intensive investigations into dose reduction, including reduced-dose imaging protocols that use computer-intensive reconstruction techniques to recover information that may otherwise be lost in the in additional noise engendered by these protocols. The FDA has been asked by sponsors to approve claims concerning reduced-dose protocols recently, and expects to see more such applications in the near future. 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 effects on diagnostic performance. However, these studies are very expensive and time consuming, and identifying small effects, or non-inferiority, requires prohibitively large sets of readers and cases. As a result, the field is increasingly turning to model-observers as an alternative to standard observer performance studies. To be effective at predicting human observer performance, model observers require extensive validation with human data in psychophysical studies. The goal of this project is performing a validation in the context of x-ray CT imaging at low doses. The aims of the project are to collect a large set of psychophysical data relevant to CT dose reduction and analyze this data using classification image methods translated from vision science to better understand how human observers perform tasks in these noisy images.Technical DescriptionHow human observers perform tasks on the basis of noisy and possibly distorted images is still an open question. The investigation proposed will provide observer performance data and an approach to modelling human observers that are well beyond what has been done to date. Using the classification image methodology with both detection and localization tasks, they will have the opportunity to model how human observers utilize information in noisy CT images, and how the use of image information changes as dose is reduced and/or dose reduction methods (smoothing, etc.) are applied. The results will therefore be of interest to the broader vision science community as well as the fields of medical image perception and CT image reconstruction. The results of our studies will allow better models of observers to be used for assessing dose reduction. By providing a much more tractable model-observer study, which can be conducted in a laboratory setting without the need for a clinical readers and cases, investigators pursuing new CT imaging techniques will be more willing to evaluate and optimize dose in their studies. This is expected to result in a more widespread use of model based approaches to fine tune image dose procedures. It is also expected that the results will have impact in vision science with more insightful models of how human observers perform visual tasks in the presence of noise.