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.

项目摘要/摘要 该项目的目的是开发用于预测人类决策的新方法 图像。预期的项目结果包括对放射科医生可变性来源的新见解和 加速临床研究成像试验的高级工具。与专家读者和 已知真相的情况是评估成像技术的公认但繁重的金标准。 许多临床研究人员无法获得必要的试验资源。虚拟试验与 已经提出了Rogate模型观察者，但重要的局限性，包括主要相关性 对人类数据进行培训的估计和持续模型保留，以防止采用其宽度 - tion。对人输入的依赖最少的定量模型将大大改善临床 访问高级成像技术。我们开发这种“低资源”模型的方法将 在目标检测和估计任务中探索读者的变异性。理想的观察者（ios） GIST处理和极值理论将是起点。这些iOS最适合DE- CISIO 涉及视觉搜索。结果将是自适应观察者模型，在 与现有模型相比，人类绩效。这些新模型将测试读者的变异性是否 可以归因于定义图像结构的候选合并和认知阈值机制 感兴趣。将开发用于诊断视觉搜索任务的优点分析。我们 将测试跨放射学模式，任务，成像模型（例如，模拟 - ，模拟）的通用性（例如 Tion/患者数据）和读者课（外行/临床医生），与研究人员有关。任务将 包括位置知名，定位和联合检测估计格式。联合任务强迫 与单独的目标检测相比，更精确的信息提取；我们假设检测性能 Manance与估计技能相关，后者有助于解决结构。我们将利用我们的 为了改善统计严格的统计严谨性，要设计多重研究的虚拟试验方案。增强的随机性 具有2D和3D图像的研究的目标建模将支持目标。 IO也将允许 检查单个读者的非线性行为。项目研究与减少剂量有关 以及X射线和核医学方式的重建方法，但这些方法可以适用 更普遍。通过加速高级成像技术的临床采用，我们的模型 观察者将对临床操作和患者护理产生直接和宽度的影响。