TR&D Project 3: Virtual Readers

基本信息

批准号：
10551846
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
项目类别：
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/10551846
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 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 beneficiary biomedical imaging clinical imaging comparative deep learning deep learning model design disease diagnosis experience imaging science improved 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

项目摘要

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)，为评估提供了一种计算替代方案。这些技术需要通过临床试验，但速度慢、成本高，而且往往缺乏事实真相，而该中心将开发一个 VIT 平台来模拟实验的关键要素。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。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。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。迄今为止，大多数虚拟阅读器方法都因其焦点狭窄、不确定性而受到限制。地面真相（正常解剖和疾病）或缺乏互操作性因此，这些技术没有。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而 TRD3 主要关注虚拟读取器，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 (2) 绩效评估的依据正常解剖和疾病的可控基本事实；（3）“可估计性指数”来评估偏差和虚拟阅读器指标的精度；（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) 超越医学成像的资源； 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 TRD3 不仅能够对临床成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虚拟阅读器将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。