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

NSF/FDA SIR：模拟观察员在 CT 剂量减少评估中的表现

• 批准号: 1445737
• 负责人: Craig Abbey
• 金额: $ 10.23万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1445737&HistoricalAwards=false
• 关键词: NSF; FDA; SIR; Modeling; Observer

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.

主要研究者：修道院，克雷格K.建议：1445737标题：CT剂量降低评估中的观察者表现建模显著性随着CT成像模式的使用增加，CT成像中的剂量降低已成为医学成像中的一个重要优先事项。在过去15年中，大型综合医疗保健系统的CT扫描估计年增长率约为3倍，令人担忧的是，诱发癌症的估计高达2%。这激发了对剂量减少的深入研究，包括使用计算机密集型重建技术来恢复信息的减少剂量成像协议，否则这些协议可能会在这些协议产生的额外噪声中丢失。FDA最近被申办者要求批准关于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，这些技术寻求保持诊断质量，对诊断性能几乎没有或没有可测量的影响。使用受试者工作特征（ROC）方法的临床阅片者研究是评价对诊断性能影响的公认标准。然而，这些研究非常昂贵和耗时，并且识别小的效应或非劣效性，需要大量的读者和病例。因此，该领域越来越多地转向模型观察员作为替代标准的观察员的性能研究。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学研究中使用人类数据进行广泛的验证。本项目的目标是在低剂量X射线CT成像的背景下进行确认。该项目的目的是收集大量的心理物理数据相关的CT剂量减少和分析这些数据使用分类图像的方法翻译从视觉科学，以更好地了解人类观察员如何执行任务，在这些嘈杂的images.Technical DescriptionHow人类观察员执行任务的基础上的嘈杂和可能扭曲的图像仍然是一个悬而未决的问题。拟议的调查将提供观察员的性能数据和建模人类观察员的方法，远远超出了迄今为止所做的工作。使用具有检测和定位任务的分类图像方法，他们将有机会建模人类观察者如何利用有噪CT图像中的信息，以及图像信息的使用如何随着剂量减少和/或剂量减少方法（平滑等）而变化。）应用。因此，研究结果将对更广泛的视觉科学界以及医学图像感知和CT图像重建领域产生影响。我们的研究结果将使更好的观察者模型用于评估剂量减少。通过提供一个更易于处理的模型-观察者研究，可以在实验室环境中进行，而不需要临床阅片师和病例，追求新的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。预计这些结果将对视觉科学产生影响，并对人类观察者在噪声存在下如何执行视觉任务产生更有见地的模型。