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

定量图像特征（QIF），如放射性和深度特征，具有巨大的潜力，以改善 各种疾病的检测、诊断和治疗评估。从计算的QIF中提取QIF时， 断层摄影（CT）扫描，计算值可以基于CT采集和重建中的差异而变化 参数，包括辐射剂量水平、切片厚度、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 模型是在上面训练的。以前的研究表明，CT的差异 采集和重建对放射组学特征值的再现性和 AI/ML模型。然而，缺乏现实世界的数据集，使AI/ML开发人员和研究人员能够 可以轻松地利用这些差异来训练和验证模型。的目的 补充是为了改善真实世界患者CT数据集的AI/ML准备，促进对以下问题的研究： 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R 01项目（R 01 EB 031993，CT影响标准化计算工具包 定量图像特征的获取和重建），其目的是了解这些 下游AI/ML模型和临床任务的变化（例如，结节检测、中风表征）以及 制定有效的方法来协调形象。该项目将汇集信息学方面的专门知识， 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CT数据集 接受肺癌筛查的患者的胸部CT扫描和100例患者的非增强头部CT 疑似中风每次扫描都将通过改变剂量、切片厚度和内核进行重建， 超过30种不同版本的扫描结果扫描也将被注释（例如，结核边界轮廓）， 与临床信息相关联（例如，结节特征、病理学证实的肺癌诊断）。 遵循FAIR原则，将使用已建立的通用数据发布临床数据、扫描和注释 元素和标准，如DICOM分割对象。在目标2中，我们将展示这种方法的实用性。 数据集作为评估AI/ML算法可靠性和鲁棒性的基准。我们将使用 基准CT数据集，以评估肺结节检测的公开可用算法的性能 以及表征和缺血体积估计。我们将评估这些算法的鲁棒性 使用诸如灵敏度和假阳性/扫描（结节检测）、 受试者工作特征曲线（结节分类）和平均绝对误差（卒中量化） 不同的扫描。该项目的成功完成将产生一个独特的数据集， 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.