Computational Toolkit for Normalizing the Impact of CT Acquisition and Reconstruction on Quantitative Image Features

用于标准化 CT 采集和重建对定量图像特征影响的计算工具包

• 批准号: 10426507
• 负责人: William Hsu
• 金额: $ 61.2万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-13 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10426507
• 关键词: Address; Adopted; Adoption; Affect; Agreement; Algorithms; Area; Artificial Intelligence; Characteristics; Chest; Classification; Clinical; Collaborations; Collection; Communities; Computer software; Coupled; Data; Data Set; Detection; Development; Diagnosis; Diagnostic; Disease; Disease Progression; Dose; Ensure; Fostering; Goals; Head; High Resolution Computed Tomography; Image; Image Analysis; Industry; Industry Collaboration; Infarction; Institution; Interstitial Lung Diseases; Investigation; Link; Lung nodule; Machine Learning; Malignant neoplasm of lung; Maps; Medical Imaging; Methods; Modeling; Morphology; Network-based; Nodule; Outcome; Patient-Focused Outcomes; Patients; Performance; Physics; Play; Process; Protocols documentation; Publications; Pulmonary Emphysema; Radiation Dose Unit; Reproducibility; Research; Role; Scanning; Seminal; Severity of illness; Slice; Source; Standardization; Stroke; Sum; Techniques; Texture; Thick; Thinness; Training; Translations; Variant; Visualization; Work; X-Ray Computed Tomography; base; biomarker development; biomarker validation; brain tissue; clinical practice; clinical translation; computed tomography screening; disease diagnosis; disease phenotype; improved; innovation; large datasets; machine learning algorithm; multidisciplinary; neural network; novel; open source; open source tool; prospective; quantitative imaging; radiomics; reconstruction; screening; tool

Quantitative image features (QIFs) such as radiomic and deep features hold enormous potential to improve the detection, diagnosis, and treatment assessment of a wide range of diseases. Generated from clinically acquired Computed Tomography (CT) scans, QIFs represent small pixel-wise changes that may be early indicators of disease progression. However, detecting these changes is complicated by variations in the way that CT scans are performed, including variations in acquisition and reconstruction parameters. Ensuring reproducible QIFs is a prerequisite for developing machine learning (ML) models that achieve consistent performance across different clinical settings. This project's premise is that QIFs are sensitive to CT parameters such as radiation dose level, slice thickness, reconstruction kernel, and reconstruction method. The combined interactions among these parameters result in unique image conditions, each yielding its own QIF value. Moreover, some clinical tasks and algorithms are more sensitive to differences in QIF values than others. We hypothesize that a systematic, task-dependent framework to normalize scans and mitigate the impact of variability in CT parameters will identify reproducible QIFs and yield more consistent ML models. Three interrelated innovations will be pursued in this work: 1) a novel framework for characterizing the impact of different acquisition and reconstruction parameters on QIFs and ML models using patient scans with known clinical outcomes in multiple domains; 2) a systematic approach for selecting an optimal mitigation technique and evaluating the impact of normalization; and 3) an open-source software toolkit that formalizes the process of CT normalization, addressing real-world use cases developed by academic and industry collaborators. In Aim 1, we will evaluate how multiple CT parameters influence QIF values and model performance. Utilizing metrics of agreement and a heat map-based visualization, we will determine under which image acquisition and reconstruction conditions the QIFs and model performance are consistent. In Aim 2, we will develop and validate a generative adversarial network-based approach to normalization. Our investigation will focus on targeted mitigation of the set of imaging conditions that are most relevant to a clinical task and on the optimization of how these models are trained. In Aim 3, we will engage a spectrum of external stakeholders to guide the development and adoption of a software toolkit called CT-NORM. Three distinct clinical domains will drive our efforts: lung nodule detection (which relies on identifying small regions of high contrast differences to identify nodules), interstitial lung disease quantification (which depends on characterizing texture differences), and ischemic core assessment (which relies on detecting low contrast differences in brain tissue). CT-NORM will provide the scientific community with an approach and a unified toolkit to characterize and mitigate the impact of reconstruction and acquisition parameters on QIFs and ML model performance. By addressing critical sources of variability, we will improve the process of generating QIFs and facilitate the discovery of precise and reproducible imaging phenotypes of disease.

定量图像特征(QIF)，如放射状特征和深度特征，具有巨大的潜力来改进 对多种疾病进行检测、诊断和治疗评估。从临床上获得的 计算机断层扫描(CT)，QIF代表微小的像素方向变化，可能是 疾病的发展。然而，由于CT扫描方式的不同，检测这些变化变得复杂 包括采集和重建参数的变化。确保QIF的可重复性是 开发在不同环境中实现一致性能的机器学习(ML)模型的先决条件 临床环境。该项目的前提是QIF对辐射剂量水平等CT参数敏感， 切片厚度、重建核和重建方法。它们之间的组合交互作用 参数会产生唯一的图像条件，每个参数都会产生自己的QIF值。此外，一些临床任务 而且，算法对QIF值的差异比其他算法更敏感。我们假设一个系统化的， 任务相关框架，以使扫描正常化并减轻CT参数变异性的影响，将确定 可重现的QIF，并产生更一致的ML模型。在这方面，将进行三个相互关联的创新 工作：1)描述不同采集和重建参数影响的新框架 在多个领域使用已知临床结果的患者扫描的QIF和ML模型；2)系统性 选择最佳缓解技术并评估归一化影响的方法；以及3)一个 开放源码软件工具包，用于规范CT标准化过程，解决实际使用案例 由学术和行业合作者开发。在目标1中，我们将评估多个CT参数 影响QIF值和模型性能。利用一致性度量和基于热图的可视化， 我们将确定在哪种图像采集和重建条件下，QIF和模型性能 是一致的。在目标2中，我们将开发和验证一种基于生成性对抗性网络的方法 正常化。我们的调查将集中于有针对性地缓解一组最常见的成像条件 与临床任务相关，以及如何优化这些模型的训练。在目标3中，我们将与 指导开发和采用名为CT-NORM的软件工具包。 三个不同的临床领域将推动我们的努力：肺结节检测(依赖于识别微小的 高对比度区域差异以识别结节)、间质性肺疾病量化(这取决于 关于纹理差异的特征)，以及缺血核心评估(依赖于检测低对比度 脑组织的差异)。CT-NORM将为科学界提供一种方法和一个统一的工具包 表征和缓解重建和采集参数对QIF和ML模型的影响 性能。通过解决关键的可变性来源，我们将改进生成QIF和 便于发现疾病的精确和可重复性的成像表型。