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.

抽象的 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数量巨大，要处理的对象种类繁多。 成像和伦理问题，通过临床评估和完善新兴成像技术 由于这些原因，虚拟成像试验（VIT）引起了人们极大的兴趣。 在开发和分析过程中对临床相关成像实验进行自动模拟和分析。 通过 VIT 改进新成像技术，对评估客观图像有重要需求 质量测量 (OIQM)，量化不同诊断结果图像的最佳效用 任务——独立于解释图像的观察者（人类或算法）的能力。 OIQM 可以揭示成像数据中任务相关信息的存在程度，因此可以 可能由人类观察者或其他次优的数值算法提取； 识别改进图像处理或其他技术变革的机会，从而导致改进 诊断任务的表现。 拟议研究的总体目标是通过开发下一代技术来应对这一挑战 用于计算 OIQM 的开源和与模态无关的计算方法，可量化最佳结果 成像系统的可能性能（所谓的理想性能观察者）用于临床相关 使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的诊断任务一直是医学图像质量的圣杯 评估领域，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 当应用于 VIT 时，我们的新方法将允许评估与任务相关的数量。 图像数据中的信息，并将加速有前途的新成像的细化和转化 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模型集合的生成对抗网络（AmGAN）； 2：开发评估成像技术最佳任务性能的方法；目标 3：使用 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 OIQM 的开发的计算工具将开源，这将完全开放。 以严格和临床水平评估和完善新兴医学成像技术的新途径 以前相关的不可能。