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

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 被收购和重建。确保可重复和可重现的QIF是开发预测性的必要的 在不同的临床环境中实现一致性能的模型。这个项目的前提是QIF 对CT参数敏感，如辐射剂量水平、层厚、重建核和 重建法。这些参数之间的组合交互作用导致独特的成像条件， 每一个都产生了自己的QIF值。此外，一些临床任务和算法对差异更加敏感 在QIF值上比其他国家更高。我们假设，一个系统的、依赖于任务的框架来表征 CT参数的可变性的影响并有效地缓解它们将导致更一致的QIF值和 预测模型的性能。这项工作将追求三个相互关联的创新：1)小说 表征不同采集和重建参数对QIF影响的框架 和ML模型，使用在多个领域具有已知临床结果的患者扫描；2)系统 选择最佳缓解技术和评估标准化影响的方法；以及 3)一个开放源码的软件工具包，它将CT标准化过程正规化，解决真正的- 由学术和行业合作者开发的世界用例。在目标1中，我们将评估 CT参数影响QIF值和模型性能。利用一致性度量和热图- 基于可视化，我们将确定QIF和QIF在哪些图像采集和重建条件下 模型的性能是一致的。在目标2中，我们将评估和增强标准化技术以缓解 采集和重建方面的差异的影响，针对的成像条件集最 与临床任务相关的。在目标3中，我们将邀请一系列外部利益攸关方来指导发展 以及采用一种名为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(依靠识别高对比度差异的小区域来识别结节)， 间质性肺疾病的量化(这取决于质地差异的特征)和缺血核心 评估(这依赖于检测脑组织中的低对比度差异)。CT-NORM将提供 科学界采用一种方法和统一的工具包来表征和减轻 重构和采集参数对QIF和预测模型性能的影响。通过解决关键问题 ，我们将改进生成QIF的过程，并促进发现精确和 疾病的可重复性成像表型。

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.