TR&D Project 3: Virtual Readers

摘要-TRD3：虚拟读者 该中心提出了虚拟成像试验(VITs)，这是一种评估快速发展的成像的新范式 技术，包括计算机断层扫描(CT)。VITS提供了一种替代评估的计算方法 这些技术通过临床试验进行，这些试验缓慢、昂贵，而且往往缺乏现成的事实，而 使受试者暴露在电离辐射中。该中心将开发一个VIT平台，以模拟 从虚拟患者(TRD1)到虚拟扫描仪(TRD2)再到虚拟阅读器(TRD3)的影像链。虚拟的 Reader是TRD的重点，它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的图像，例如病变检测、分类或测量。具体地说， 虚拟阅读器包括三个具有代表性的类别：观察者模型、放射组学和机器学习。 虚拟阅读器可以高效、有效地分析成像试验中的海量数据，无论是临床试验 或者是模拟的。到目前为止，大多数虚拟阅读器方法都受到其狭窄的关注范围、不确定性 基本事实(正常解剖和疾病)，或缺乏互操作性。因此，这些技术并没有 然而却被广泛地翻译。为了解决这一未得到满足的需求，TRD3将编写一套易于使用的虚拟阅读器 不仅支持VITS，还支持广泛的其他医学图像评估需求的工具。 这项工作将以三个具体目标进行：(1)为任务实施观察者模型和放射组学工具包-- 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，以及(3)将虚拟读卡器实用程序集成到统一的VIT平台中，并针对研究进行验证 真实的影像和放射科医生。而TRD3主要专注于虚拟阅读器，作为最终的技术 中心的发展项目，也将从整体上验证中心的资源。 TRD3的交付成果包括：(1)超越利基应用程序和 概括到不同的主题、系统和任务；(2)由以下人员提供信息的绩效评估 对于正常解剖和疾病都是可控的基础事实；(3)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，所有这些都对VIT至关重要；(5)超越 VITS，其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，在这种文化中，技术评估变得公平和 可重现的。TRD3将对临床影像科学和实践产生重大影响，不仅使 评估成像技术的有效方法，同时也刺激数据科学的新发展 医学成像。虚拟阅读器资源结合了无数的临床和模拟图像数据 中心将提供必要的框架，使VITs能够进行CT成像和其他方面的工作。

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.