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

用于评估医学成像系统最佳任务性能的深度学习技术

• 批准号: 10635347
• 负责人: Mark A Anastasio
• 金额: $ 38.25万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10635347
• 关键词: Acceleration; Address; Algorithms; Anatomy; Assessment tool; Case Study; Clinic; Clinical; Clinical Trials; Computing Methodologies; Data; Detection; Development; Diagnostic; Diagnostic Imaging; Effectiveness; Ethics; Evaluation; Human; Image; Image Analysis; Imaging technology; Learning; Measurement; Measures; Medical Imaging; Methods; Modality; Modeling; Modernization; Needs Assessment; Patient-Focused Outcomes; Performance; Property; Research; Specific qualifier value; System; Task Performances; Technology; Time; Translations; Variant; clinically relevant; cohort; computerized tools; deep learning; denoising; digital; experimental study; generative adversarial network; ideal observer (Bayesian); image processing; imaging system; improved; interest; multimodal data; next generation; novel; open source; public health relevance; restoration; simulation; success; ultra high resolution; virtual imaging

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.

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，对象的种类繁多 成像和伦理问题，通过临床对新兴成像技术的评估和改进 审判往往是不可能的。出于这些原因，人们对虚拟成像试验(VIT)非常感兴趣，这种试验允许 临床相关影像实验的自动模拟和分析。在开发和开发过程中 通过VITs改进新的成像技术，需要对目标图像进行评估 质量度量(OIQM)，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，就像 OIQM可以揭示与任务相关的信息在成像数据中的存在程度，因此可以 可能由人类观察者或其他次优的数值算法提取；这可以允许 确定改进图像处理或其他技术变革的机会，从而提高 诊断任务的性能。 拟议研究的广泛目标是通过发展下一代来应对这一挑战 用于计算OIQM的开放源码和与形态无关的计算方法，以量化最佳 对于临床相关的成像系统的可能性能--所谓的理想观察者性能 任务。利用现实随机性估计医学成像技术的最佳可实现性能 数字对象模型和临床相关诊断任务一直是医学图像质量的圣杯 评估领域，由于迄今缺乏成功，该领域仅限于不现实的对象模型和任务 几十年。当在VIT中使用时，我们的新方法将允许评估与任务相关的量 图像数据中的信息，并将加快有希望的新成像的提炼和转换 将技术应用于临床。该项目的具体目标是：目标1：开发和验证环境 用于创建临床相关数字幻象集合的生成性对抗网络(AMGAN)；目标 2：开发估计成像技术的最佳任务绩效的方法；目标3：使用 开发了评估基于深度学习的图像恢复的工具。 开发的用于计算OIQM的计算工具将是开源的。这将完全打开 评估和改进具有严谨和临床水平的新兴医学成像技术的新途径 相关性以前是不可能的。