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的方式，CT扫描 包括采集和重建参数的变化。确保可重现的QIF是 这是开发机器学习（ML）模型的先决条件，这些模型可以在不同的环境中实现一致的性能。 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。这些因素之间的相互作用 参数产生唯一的图像条件，每个参数产生其自己的QIF值。此外，一些临床任务 并且算法对QIF值的差异比其它算法更敏感。我们假设一个系统的， 规范扫描和减轻CT参数变化影响的任务相关框架将识别 可重复的QIF，并产生更一致的ML模型。在这方面，将进行三项相互关联的创新。 工作：1）一种新的框架，用于表征不同采集和重建参数的影响 在QIF和ML模型上使用患者扫描，在多个领域中具有已知的临床结果; 2）系统性 选择最佳缓解技术和评估正常化影响的方法;以及3） 一个开源软件工具包，正式确定了CT标准化的过程，解决了现实世界的用例 由学术界和工业界合作者共同开发。在目标1中，我们将评估多个CT参数 影响QIF值和模型性能。利用一致性指标和基于热图的可视化， 我们将确定在何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对抗网络的方法， 标准化我们的调查将集中在有针对性的缓解一组成像条件， 与临床任务相关，并优化这些模型的训练方式。在目标3中，我们将与一个 我们还邀请了一系列外部利益相关者，以指导开发和采用一个名为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：肺结节检测（依赖于识别小的 识别结节的高对比度差异区域）、间质性肺病量化（这取决于 表征纹理差异）和缺血核心评估（其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统一的工具包 表征和减轻重建和采集参数对QIF和ML模型的影响 性能通过解决可变性的关键来源，我们将改进生成QIF的过程， 有助于发现疾病的精确和可重复的成像表型。

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 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.