An AI/ML-ready Dataset for Investigating the Effect of Variations in CT Acquisition and Reconstruction

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 各种疾病的检测、诊断和治疗评估。从计算的QIF中提取QIF时 断层扫描(CT)，计算值可能会根据CT采集和重建的不同而不同 参数，包括辐射剂量水平、层厚、重建核和重建方法。 人工智能(AI)和机器学习(ML)模型的性能取决于数据的多样性 模型就是在这个模型上训练的。以前的研究已经表明，CT的差异会产生负面影响 采集和重建对放射学特征值的重复性和性能有影响 AI/ML模型。然而，缺乏能够使AI/ML开发人员和研究人员能够使用的真实数据集 可以轻松地利用来训练和验证对这些差异具有健壮性的模型。这样做的目的是 补充是提高真实世界患者CT数据集的AI/ML准备情况，促进对 表征和减轻CT采集和重建参数变化的影响。这个项目 基于我们的父R01项目(R01 EB031993，用于标准化CT影响的计算工具包 定量图像特征的获取和重建)，其目的是了解这些特征的影响 下游AI/ML模型和临床任务(例如，结节检测、卒中特征)和 制定有效的图像协调方法。这个项目将汇集信息学方面的专业知识， 医学物理学和数据/模型共享标准。在目标1中，我们将发布200个AI/ML就绪的CT数据集 肺癌筛查患者的胸部CT扫描及100例平扫患者的头颅CT 疑似中风。每次扫描将通过不同的剂量、层厚和核进行重建，结果是 同一扫描的30多个不同版本。还将为扫描添加注释(例如，勾勒出的结节边界)和 与临床信息相联系(例如，结节特征、经病理证实的肺癌诊断)。 遵循公平原则，临床数据、扫描和注释将使用已建立的通用数据发布 元素和标准，例如DICOM分割对象。在目标2中，我们将演示这一功能的实用性 数据集作为评估AI/ML算法可靠性和稳健性的基准。我们将使用 基准CT数据集用于评估公开可用的肺结节检测算法的性能 以及特征化和缺血量估计。我们将评估这些算法的健壮性 使用敏感度和假阳性/扫描(结节检测)等指标的性能，位于 接收器工作特性曲线(结节分类)和平均绝对误差(行程量化) 在不同的扫描中。该项目的成功完成将产生一个独特的数据集，该数据集将使 可用于改进与图像重建相关的AI/ML算法的可用真实世界患者数据， 恢复/协调以及下游任务。

Quantitative image features (QIFs) such as radiomic and deep features hold enormous potential to improve the detection, diagnosis, and treatment assessment of various diseases. When extracting QIFs from computed tomography (CT) scans, computed values can vary based on differences in CT acquisition and reconstruction parameters, including radiation dose level, slice thickness, reconstruction kernel, and reconstruction method. The performance of artificial intelligence (AI) and machine learning (ML) models depends on the diversity of data on which the model was trained. Previous studies have shown the negative impact that differences in CT acquisition and reconstruction have on the reproducibility of radiomic feature values and the performance of AI/ML models. However, there is a dearth of real-world datasets that enable AI/ML developers and researchers can easily leverage to train and validate models that are robust to these differences. The objective of this supplement is to improve the AI/ML-readiness of real-world patient CT datasets, facilitating investigations into characterizing and mitigating the effect of variations in CT acquisition and reconstruction parameters. This project builds upon our parent R01 project (R01 EB031993, Computational Toolkit for Normalizing the Impact of CT Acquisition and Reconstruction on Quantitative Image Features), which aims to understand the effect of these variations on downstream AI/ML models and clinical tasks (e.g., nodule detection, stroke characterization) and develop effective methods for image harmonization. This project will bring together expertise in informatics, medical physics, and data/model sharing standards. In Aim 1, we will release an AI/ML-ready CT dataset of 200 chest CT scans of patients who underwent lung cancer screening and 100 non-contrast head CTs of patients with suspected stroke. Each scan will be reconstructed by varying dose, slice thickness, and kernel, resulting in over 30 different versions of the same scan. Scans will also be annotated (e.g., outlined nodule boundaries) and linked with clinical information (e.g., nodule characteristics, pathology-confirmed lung cancer diagnosis). Following FAIR principles, clinical data, scans, and annotations will be released using established common data elements and standards such as DICOM segmentation objects. In Aim 2, we will demonstrate the utility of this dataset as a benchmark for assessing the reliability and robustness of AI/ML algorithms. We will use the benchmark CT dataset to evaluate the performance of publicly available algorithms for lung nodule detection and characterization and ischemic volume estimation. We will assess the robustness of these algorithms’ performance using metrics such as sensitivity and false positives/scan (nodule detection), area under the receiver operating characteristic curve (nodule classification), and mean absolute error (stroke quantification) across different scans. Successful completion of this project will result in a unique dataset that would double the available real-world patient data that can be used to improve AI/ML algorithms related to image reconstruction, restoration/harmonization, and downstream tasks.