Deep learning technologies for estimating the optimal task performance of medical imaging systems

摘要 现代医学成像系统包括复杂的硬件和复杂的计算方法。 考虑到影响图像质量的系统参数的绝对数量，将被处理的对象的大量变化是可能的。 成像和伦理问题，通过临床评估和改进新兴成像技术， 审判往往是不可能的。由于这些原因，人们对虚拟成像试验（VITs）非常感兴趣， 临床相关成像实验的自动模拟和分析。在开发和 通过VITs改进新的成像技术，非常需要评估客观图像 质量指标（OIQMs），量化结果图像对不同诊断的最佳效用 任务-独立于解释图像的观察者（人或算法）的能力。实际上，这种 OIQMs可以揭示任务相关信息在成像数据中存在的程度，因此可以 可能由人类观察者或次优的其他数值算法提取;这可以允许 识别改进图像处理或其他技术变化的机会， 执行诊断任务。 拟议研究的广泛目标是通过开发下一代来应对这一挑战 开源和模态不可知的计算方法，用于计算OIQMs， 成像系统的可能性能-所谓的理想观察者性能-对于临床相关 任务使用真实随机的医学成像技术的最佳可实现性能的估计 数字对象幻影和临床相关的诊断任务已经成为医学图像质量的圣杯 评估领域，并且迄今为止缺乏成功将该领域限制在不切实际的对象模型和任务上， 几十年当在VITs中使用时，我们的新方法将允许评估任务相关的 图像数据中的信息，并将加速有前途的新成像技术的改进和转化。 技术到诊所。该项目的具体目标是：目标1：开发和验证环境 生成对抗网络（AmGANs），用于创建临床相关数字幻影的集合;目的 2：开发用于估计成像技术的最佳任务性能的方法; 开发了用于评估基于深度学习的图像恢复的工具。 用于计算OIQMs的开发计算工具将开放源代码。这将完全打开 评估和完善新兴医学成像技术的新途径，具有严格和临床水平 以前不可能的相关性。

ABSTRACT Modern medical imaging systems comprise complicated hardware and sophisticated computational methods. Given the sheer number of system parameters that impact image quality, the large variety in objects to be imaged, and ethical concerns, the assessment and refinement of emerging imaging technologies via clinical trials often is impossible. For these reasons, there is great interest in virtual imaging trials (VITs) that permit the automated simulation and analysis of clinically relevant imaging experiments. During the development and refinement of new imaging technologies via VITs, there is an important need for assessing objective image quality measures (OIQMs) that quantify the best possible utility of the resulting images for different diagnostic tasks—independent of the ability of the observer (human or algorithm) who interprets the images. In effect, such OIQMs can reveal the extent to which task-related information is present in imaging data and thus can be potentially extracted by a human observer or other numerical algorithm that is sub-optimal; this can permit the identification of opportunities for improved image processing or other technology changes that lead to improved performance on diagnostic tasks. The broad objective of the proposed research is to address this challenge by developing the next generation of open source and modality-agnostic computational methods for computing OIQMs that quantify the best possible performance of an imaging system—the so-called ideal observer performance—for clinically relevant tasks. Estimation of the best achievable performance of medical imaging technologies using realistic stochastic digital object phantoms and clinically relevant diagnostic tasks has been a holy grail for the medical image-quality assessment field, and the lack of success to date has limited the field to unrealistic object models and tasks for decades. When employed in VITs, our new methods will permit assessment of the amount of task-relevant information in image data and will accelerate the refinement and translation of promising new imaging technologies to the clinic. The Specific Aims of the project are: Aim 1: To develop and validate ambient generative adversarial networks (AmGANs) for creating ensembles of clinically relevant digital phantoms; Aim 2: To develop methods for estimating the optimal task performance of an imaging technology; Aim 3: To use the developed tools for assessing deep learning-based image restoration. The developed computational tools for computing OIQMs will be made open source. This will open entirely new avenues for assessing and refining emerging medical imaging technologies with a level of rigor and clinical relevance previously not possible.