TR&D Project 3: Virtual Readers

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• 批准号: 10372911
• 负责人: JOSEPH Y LO
• 金额: $ 31.44万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10372911
• 关键词: Address; Adoption; Algorithms; Anatomy; Artificial Intelligence; Benchmarking; Categories; Classification; Clinical; Clinical Data; Clinical Trials; Communities; Complex; Computer Assisted; Data; Data Science; Data Set; Databases; Detection; Development; Disease; Education; Elements; Ensure; Evaluation; Future; Human; Image; Image Analysis; Imaging Phantoms; Imaging technology; Institution; Ionizing radiation; Lesion; Machine Learning; Manufacturer Name; Mathematics; Measurement; Measures; Medical; Medical Imaging; Modality; Modeling; Morphology; Patients; Perception; Performance; Phase; Protocols documentation; Radiation Dose Unit; Reader; Reading; Reproducibility; Research; Resource Development; Resources; Scientist; Speed; System; Technology; Technology Assessment; Testing; Texture; Training; Transcend; Translating; Uncertainty; Variant; Work; X-Ray Computed Tomography; artificial intelligence algorithm; base; beneficiary; biomedical imaging; clinical imaging; comparative; deep learning; deep learning model; design; disease diagnosis; experience; imaging science; indexing; interoperability; machine learning model; member; model design; open data; quantitative imaging; radiologist; radiomics; research clinical testing; technology development; technology research and development; tool; trend; virtual; virtual imaging; virtual patient; virtual platform

ABSTRACT – TRD3: Virtual Readers The Center proposes virtual imaging trials (VITs), a new paradigm to evaluate rapidly advancing imaging technologies, including computed tomography (CT). VITs offer a computational alternative to the evaluation of these technologies through clinical trials, which are slow, expensive, and often lack ground truth, while exposing subjects to ionizing radiation. The Center will develop a VIT platform to emulate key elements of the imaging chain from virtual patients (TRD1) to virtual scanners (TRD2) to virtual readers (TRD3). The virtual reader, the focus of this TRD, are defined as image analysis tools that emulate and extend the clinical reading of images for specific tasks or needs such as lesion detection, classification, or measurement. Specifically, the virtual readers comprise three representative categories: observer models, radiomics, and machine learning. Virtual readers can efficiently and effectively analyze the vast amounts of data in imaging trials, be they clinical or simulated. To date, most virtual reader approaches have been limited by their narrow focus, uncertainty of ground truth (normal anatomy and disease), or lack of interoperability. As a result, these technologies have not yet been translated broadly. To address this unmet need, TRD3 will codify a suite of easy-to-use virtual reader tools to enable not only VITs but also a wide range of other medical image evaluation needs. This work will proceed in three Specific Aims: (1) implement an observer model and radiomics toolset for task- based assessment of CT images, (2) create deep learning resources for analysis and processing of CT images, and (3) integrate virtual reader utilities into a unified VIT platform and validate it against studies with real images and radiologists. While TRD3 focuses primarily on virtual readers, as the final technology development project of the Center, it will also validate Center resources as a whole. The deliverables of TRD3 include the following: (1) virtual reader tools that go beyond niche applications and generalize to different subjects, systems, and tasks; (2) performance assessment that is informed by controllable ground truth for both normal anatomy and disease; (3) “estimability index” to assess bias and precision of virtual reader metrics; (4) machine learning tools that perform disease detection and classification as well as data augmentation, all of which are crucial to VITs; (5) resources for medical imaging that transcend VITs with applications including clinical evaluation and education, and (6) benchmark databases and performance levels that facilitate a culture of open science where technology assessment becomes fair and reproducible. TRD3 will have a significant impact on clinical imaging science and practice by not only enabling effective ways of evaluating imaging technology but also spurring new developments in data science for medical imaging. The virtual reader resources combined with myriad clinical and simulated image data of the Center will provide the essential framework to enable VITs in CT imaging and beyond.

摘要 – TRD3：虚拟读者 该中心提出虚拟成像试验（VIT），这是一种评估快速发展的成像的新范例 技术，包括计算机断层扫描 (CT)。 VIT 提供了一种计算替代方案来评估 这些技术通过临床试验进行，但速度慢、成本高，而且往往缺乏事实真相，而 将受试者暴露于电离辐射下。该中心将开发一个 VIT 平台来模拟 从虚拟患者 (TRD1) 到虚拟扫描仪 (TRD2) 再到虚拟读取器 (TRD3) 的成像链。虚拟的 阅读器是本 TRD 的焦点，被定义为模拟和扩展临床阅读的图像分析工具 用于特定任务或需求（例如病变检测、分类或测量）的图像。具体来说， 虚拟读者包括三个代表性类别：观察者模型、放射组学和机器学习。 虚拟阅读器可以高效、有效地分析影像试验中的大量数据，无论是临床试验还是临床试验。 或模拟。迄今为止，大多数虚拟阅读器方法都因其关注点狭窄、信息的不确定性而受到限制。 基本事实（正常解剖结构和疾病），或缺乏互操作性。结果，这些技术并没有 尚未被广泛翻译。为了解决这一未满足的需求，TRD3 将编写一套易于使用的虚拟阅读器 工具不仅可以满足 VIT，还可以满足广泛的其他医学图像评估需求。 这项工作将按照三个具体目标进行：（1）为任务实施观察者模型和放射组学工具集 基于CT图像的评估，（2）创建用于CT分析和处理的深度学习资源 图像，(3) 将虚拟阅读器实用程序集成到统一的 VIT 平台中，并根据研究进行验证 真实图像和放射科医生。虽然 TRD3 主要关注虚拟阅读器，但作为最终技术 中心的发展项目，也将验证中心的整体资源。 TRD3 的交付成果包括：(1) 超越利基应用程序的虚拟阅读器工具 推广到不同的主题、系统和任务； (2) 绩效评估的依据 正常解剖和疾病的可控基础事实； (3)“可估计性指数”来评估偏差和 虚拟读者指标的精确度； (4) 执行疾病检测和分类的机器学习工具 以及数据增强，所有这些对于 VIT 都至关重要； (5) 超越医学影像的资源 VIT 的应用包括临床评估和教育，以及 (6) 基准数据库和 促进开放科学文化的绩效水平，使技术评估变得公平和 可重现。 TRD3 不仅能够对临床影像科学和实践产生重大影响 评估成像技术的有效方法，同时也刺激了数据科学的新发展 医学成像。虚拟阅读器资源与大量临床和模拟图像数据相结合 该中心将提供必要的框架，以在 CT 成像及其他领域实现 VIT。