CBCT-IQ：用于图像质量评估与基准测试的公开标注锥形束CT数据集
CBCT-IQ: A Publicly Available Annotated Cone-Beam CT Dataset for Image Quality Assessment and Benchmarking
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中文总结 AI 辅助
本研究构建首个公开标注的CBCT-IQ数据集，含1764张经专家评分的CBCT图像，对26种IQA指标基准测试并提出排名方法，为CBCT IQA研究提供标准化资源。
中文摘要 AI 辅助
医学图像质量对诊断准确性至关重要，尤其在基于X射线的成像模态（如锥形束计算机断层扫描（CBCT））中，需平衡图像质量与辐射剂量。尽管专家视觉评估仍是图像质量评估的临床标准，但该方法耗时、主观且受观察者间差异影响，凸显了对可靠的定量图像质量评估（IQA）方法的需求。然而，此类IQA方法的开发与验证因缺乏带专家图像质量标注的公开CBCT数据集而受限。本研究提供首个开放获取的CBCT IQA数据集，包含1764张经标注的图像切片，这些切片通过系统改变图像采集与重建参数获取。三名临床专家采用四级评分方案对整体图像质量及预定义的感兴趣区域（ROI）进行评分。此外，我们将26种全参考和无参考IQA指标与专家标注进行基准测试，并引入一种基于IQA指标的探索性排名方法，该方法能够区分细微的图像质量差异。该数据集为未来CBCT IQA研究提供了标准化基准，为新型IQA方法的开发与验证提供了宝贵资源，支持可复现研究并推动CBCT IQA领域的发展。
英文摘要
Medical image quality plays a critical role in diagnostic accuracy, especially in X-ray-based imaging modalities such as cone-beam computed tomography (CBCT), where image quality must be balanced against radiation dose. While expert visual evaluation remains the clinical standard for image quality evaluation, it is time-consuming, subjective and affected by inter-observer variability, emphasizing the need for reliable quantitative image quality assessment (IQA) methods. However, the development and validation of such IQA methods have been limited by the lack of publicly available CBCT datasets with expert image quality annotations. In this study, we provide the first open-access CBCT IQA dataset containing 1,764 annotated image slices acquired using systematic variations in image acquisition and reconstruction parameters. Three clinical experts graded the overall image quality and a predefined regions of interest (ROI) using a four-level scoring scheme. In addition, we benchmark 26 full reference- and no reference-based IQA measures against expert annotations and introduce an exploratory IQA measure-based ranking capable of distinguishing subtle image quality differences. This dataset introduced a standardized benchmark for future CBCT IQA research and provides a valuable resource for the development and validation of new IQA methods, enabling reproducible research and advancing CBCT IQA.
发表机构
- Austrian Center for Medical Innovation and Technology (ACMIT)（奥地利医学创新与技术中心（ACMIT））
- Danube Private University（多瑙河私立大学）
- Medical University of Vienna（维也纳医科大学）
- University of Cambridge（剑桥大学）
- University of Applied Sciences Technikum Wien（维也纳技术应用科学大学）
- University Hospital Vienna（维也纳大学医院）
- Montpellier Cancer Institute（蒙彼利埃癌症研究所）
- University of Montpellier（蒙彼利埃大学）
- Urmia University of Medical Science（乌尔米耶医科大学）
- University Hospital Wiener Neustadt（维也纳新城大学医院）
- University of Sydney（悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。
