Visual-search ideal observers for modeling reader variability

视觉搜索理想观察者对读者变异性进行建模

• 批准号: 10530899
• 负责人: Howard Carl Gifford
• 金额: $ 57.73万
• 依托单位: UNIVERSITY OF HOUSTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10530899
• 关键词: Adopted; Adoption; Agreement; Behavior; Clinical; Clinical Research; Cognitive; Computer Models; Data; Data Set; Dependence; Detection; Diagnostic; Diagnostic Imaging; Digital Breast Tomosynthesis; Discipline of Nuclear Medicine; Dose; Goals; Gold; Human; Hybrids; Image; Imaging technology; Individual; Information Retrieval; Joints; Kidney; Location; Methods; Modality; Modeling; Outcome; Patient Care; Patient Simulation; Patients; Performance; Physiological; Primates; Process; Property; Protocols documentation; Radiology Specialty; Reader; Reading; Research; Research Personnel; Resources; Roentgen Rays; Sampling; Scanning; Source; Structure; Study models; Task Performances; Technology; Testing; Three-Dimensional Image; Training; Validation; Variant; Work; clinical diagnostics; clinical imaging; clinically relevant; cost; flexibility; human data; ideal observer (Bayesian); imaging study; improved; in silico; insight; interest; novel; operation; prevent; radiologist; reconstruction; sample fixation; single photon emission computed tomography; skills; theories; tool; virtual; visual search

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.

项目概要/摘要 该项目的目标是开发通过诊断来预测人类决策的新方法 预期的项目成果包括对放射科医生变异性来源的新见解和 加速临床研究中的成像试验的先进工具。此类试验由专家读者和专家共同完成。 已知真相案例是评估成像技术的公认但繁琐的黄金标准。 许多临床研究人员无法获得必要的试验资源。 已经提出了 rogate 模型观察者，但存在重要的局限性，包括主要是相关的 估计和模型持续依赖人类数据进行训练，阻碍了它们的广泛采用 对人类输入的依赖性最小的定量模型将大大改善临床效果。 我们开发这种“低资源”模型的方法将获得先进的成像技术。 探索目标检测和估计任务中的读者变异性。 要点处理和极值理论将是这些 IO 的最佳出发点。 最大化提取的特征值集的决策过程，这是任务的共同前提 视觉搜索的结果将是产生更严格界限的自适应观察者模型。 与现有模型相比，这些新模型将测试读者的变异性。 可以归因于定义图像结构的候选池和认知阈值机制 我们将开发诊断视觉搜索任务的分析品质因数。 将测试模型在放射学模式、任务、成像模型（例如模拟 化/患者数据）和读者类别（非专业人士/临床医生），所有这些任务都与研究人员相关。 包括位置已知、定位和联合检测估计格式。联合任务强制。 比单独的目标检测更精确的信息提取；我们追求检测性能 曼斯与估计技能相关，后者有助于解决结构问题。 研究结果设计了多读者虚拟试验方案，以提高统计的严谨性。 2D 和 3D 图像研究的目标建模也将成为支持目标。 对个别读者的非线性行为进行检查 该项目研究与剂量减少有关。 X 射线和核医学模式的重建方法，但这些方法可以应用 更广泛地说，通过加速先进成像技术的临床应用，我们的模型。 观察员将对临床操作和患者护理产生直接而广泛的影响。