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: 10530062
• 负责人: William Hsu
• 金额: $ 60.36万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10530062
• 关键词: Academia; Address; Adopted; Adoption; Affect; Agreement; Algorithms; Area; Characteristics; Chest; Classification; Clinical; Clinical Trials; Collaborations; Collection; Communities; 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; Link; Lung nodule; Machine Learning; Malignant neoplasm of lung; Maps; Medical Imaging; Methods; Modeling; Morphology; Nodule; Outcome; Patient-Focused Outcomes; Patients; Performance; Photons; Physics; Play; Process; Protocols documentation; Publications; Pulmonary Emphysema; Radiation Dose Unit; Reproducibility; Research; Resources; Role; Scanning; Seminal; Severity of illness; Slice; Software Tools; Source; Standardization; Stroke; Sum; Surrogate Endpoint; Techniques; Technology; Texture; Thick; Thinness; Training; Variant; Visualization; Work; X-Ray Computed Tomography; base; biomarker development; biomarker validation; brain tissue; clinical practice; clinical translation; computed tomography screening; disease phenotype; improved; innovation; large datasets; machine learning model; multidisciplinary; neural network; novel; open source; open source tool; predictive modeling; 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 how CT scans are acquired and reconstructed. Ensuring repeatable and reproducible QIFs is necessary for developing predictive 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 characterize the impact of variability in CT parameters and effectively mitigate them will result in more consistent QIF values and the performance of prediction 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 assess and enhance normalization techniques for mitigating the impact of differences in acquisition and reconstruction, targeting the set of imaging conditions that are most relevant to a clinical task. 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 prediction 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），如放射性和深度特征，具有巨大的潜力，以改善 检测、诊断和治疗评估多种疾病。由临床获得的 计算机断层扫描（CT），QIF代表小的像素变化，可能是早期指标， 疾病进展。然而，检测这些变化是复杂的变化，在如何CT扫描是 获取并重建。确保可重复和可再现的QIF对于开发预测性 在不同的临床环境中实现一致性能的型号。这个项目的前提是， 对CT参数敏感，如辐射剂量水平、切片厚度、重建核， 重建方法这些参数之间的组合相互作用导致独特的图像条件， 每一个产生其自己的QIF值。而且，一些临床任务和算法对差异更敏感 在QIF值中，我们假设，一个系统的，依赖于任务的框架来描述 CT参数变化的影响，并有效地减轻它们将导致更一致的QIF值， 预测模型的性能。本书将追求三个相互关联的创新：1）一部小说 用于表征不同采集和重建参数对QIF影响的框架 和ML模型，使用患者扫描，在多个领域中具有已知的临床结果; 2）系统的 选择最佳缓解技术和评估正常化影响的方法; 3)一个开源的软件工具包，正式的CT标准化的过程，解决真实的- 由学术界和行业合作者开发的全球用例。在目标1中，我们将评估 CT参数影响QIF值和模型性能。利用协议指标和热图- 基于可视化，我们将确定在哪些图像采集和重建条件下QIF和 模型的性能是一致的。在目标2中，我们将评估和增强规范化技术， 在采集和重建中的差异的影响，针对成像条件的集合， 与临床任务相关。在目标3中，我们将邀请一系列外部持份者来指导发展 并采用了一个名为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： 结节检测（其依赖于识别高对比度差异的小区域以识别结节）， 间质性肺病量化（取决于表征纹理差异）和缺血性核心 评估（其依赖于检测脑组织中的低对比度差异）。CT-NORM将提供 科学界提供一种方法和一个统一的工具包， QIF的重建和采集参数以及预测模型性能。通过解决关键问题， 变异性的来源，我们将改进生成QIF的过程，并促进发现精确和 疾病的可重复成像表型。