Visual-search ideal observers for modeling reader variability

项目摘要/摘要 这个项目的目标是开发新的方法来预测人类的决策和诊断 图像。预期的项目成果包括对放射科医生可变性来源的新见解和 在临床研究中加速影像试验的先进工具。这类试验由专家读者和 已知真相案例是评估成像技术的公认但繁重的黄金标准。 许多临床研究人员无法获得必要的试验资源。与Sur-的虚拟试验 已经提出了罗盖特模型观察者，但存在重要限制，包括主要相关的 估计和持久的模型依赖于人类数据进行训练，防止其广泛传播的ADoP- 提顿。对人类输入的依赖程度最低的量化模型将显著改善临床 获得先进的成像技术。我们开发这种“低资源”模型的方法将 探索读者在目标检测和估计任务中的可变性。派生自理想观察者(IO) 主旨处理和极值理论将是本文的起点。这些IO最适合 在提取的特征值集上最大化的精确度过程，这是任务的共同前提 涉及视觉搜索。其结果将是自适应观察者模型，它将产生更严格的边界 人类性能与现有模型的比较。这些新模型将测试读者的可变性 可以归因于候选集合和认知阈值机制，即define图像结构- 真正感兴趣的是。将开发用于诊断视觉搜索任务的Analyticfi标准。我们 将测试模型在放射学模式、任务、成像模型(例如，模拟- 医生/患者数据)和读者类别(门外汉/临床医生)，所有这些都与研究人员相关。这些任务将 包括位置已知、定位和联合检测-估计格式。联合任务迫使 比单独的目标检测更精确的信息提取；我们假设检测执行- Mance与估计技能相关，后者有助于解决结构问题。我们将利用我们的 fi计划设计多人虚拟试验方案，以提高统计的严谨性。增强的随机性 使用2D和3D图像进行研究的目标建模将支持AIMS。IO还将允许 针对个别读者的非线性行为的考察。项目研究与减少剂量有关 以及用于x射线和核医学模式的重建方法，但这些方法可以应用 更笼统地说。通过加快先进成像技术的临床应用，我们的模型 观察员将对临床手术和病人护理产生直接和广泛的影响。

Project Summary/Abstract The goal of this project is to develop novel methods for predicting human decisions with diagnostic images. Expected project outcomes include new insights into sources of radiologist variability and advanced tools to accelerate imaging trials in clinical research. Such trials with expert readers and known-truth cases are an accepted but burdensome gold standard for evaluating imaging technology. The necessary trial resources are not available to many clinical researchers. Virtual trials with sur- rogate model observers have been proposed, but important limitations, including primarily correlative estimates and persistent model reliance on human data for training, prevent their widespread adop- tion. Quantitative models with minimal dependence on human input will substantially improve clinical access to advanced imaging technology. Our approach to develop such “low-resource” models will explore reader variability in target detection and estimation tasks. Ideal observers (IOs) derived from gist-processing and extreme-value theories will be the starting point. These IOs are optimal for de- cision processes that maximize over sets of extracted feature values, a common premise for tasks involving visual search. The result will be adaptive observer models that produce tighter bounds on human performance compared to existing models. These new models will test if reader variability can be attributed to candidate pooling and cognitive threshold mechanisms that define image struc- ture of interest. Analytic figures of merit for diagnostic visual-search tasks will be developed. We will test model generalizability across radiological modalities, tasks, imaging models (e.g., simula- tion/patient data), and reader classes (lay/clinician), all of relevance for researchers. The tasks will include location-known, localization, and joint detection-estimation formats. The joint task compels more precise information extraction than target detection alone; we hypothesize that detection perfor- mance correlates with estimation skill, with the latter helping to resolve structure. We shall leverage our findings to devise multireader virtual trial protocols for improved statistical rigor. Enhanced stochastic target modeling for studies with 2D and 3D images will be supporting aims. The IO will also allow examination of nonlinear behaviors for individual readers. The project studies relate to dose reduction and reconstruction methods for x-ray and nuclear medicine modalities, but the methods can apply more generally. By accelerating the clinical adoption of advanced imaging technology, our model observers will have a direct and widespread impact on clinical operations and patient care.