TR&D Project 3: Virtual Readers

摘要-TRD 3：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技术的新模式。 技术，包括计算机断层扫描（CT）。VITs提供了一个计算替代评估 这些技术通过临床试验，这是缓慢的，昂贵的，往往缺乏地面真理，而 使受试者暴露于电离辐射。该中心将开发一个虚拟信息技术平台， 从虚拟患者（TRD 1）到虚拟扫描仪（TRD 2）再到虚拟阅读器（TRD 3）的成像链。虚拟 阅读器（本TRD的重点）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的图像，例如病变检测、分类或测量。具体而言是 虚拟阅读器包括三个代表性类别：观察者模型、放射组学和机器学习。 虚拟阅读器可以有效地分析成像试验中的大量数据，无论是临床试验还是临床试验， 或模拟的。到目前为止，大多数虚拟阅读器方法都受到其狭隘的关注点， 基础事实（正常解剖和疾病），或缺乏互操作性。因此，这些技术并没有 被广泛翻译。为了满足这一未满足的需求，TRD 3将编制一套易于使用的虚拟阅读器 这些工具不仅能够满足VITs的需求，还能满足广泛的其他医学图像评估需求。 这项工作将在三个具体目标：（1）实现一个观察员模型和放射组学工具集的任务- 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， 真实的图像和放射科医生。虽然TRD 3主要关注虚拟阅读器，但作为最终技术， 作为中心的一个发展项目，它也将验证中心的资源作为一个整体。 第三次技术研究报告的成果包括：（1）超越特定应用的虚拟阅读工具， 概括到不同的科目、系统和任务;（2）绩效评估， 正常解剖结构和疾病的可控基础事实;（3）评估偏倚的“可估计性指数”， 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，所有这些都是至关重要的VITs;（5）资源的医疗成像，超越 应用包括临床评估和教育在内的VITs，以及（6）基准数据库， 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， 可复制的TRD 3将对临床成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，同时也促进了数据科学的新发展， 医学成像虚拟读者资源结合了大量的临床和模拟图像数据， 中心将提供必要的框架，使VITs在CT成像和超越。

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.