CBCT-IQ: A Publicly Available Annotated Cone-Beam CT Dataset for Image Quality Assessment and Benchmarking
Organizations: Austrian Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria · Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria · Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria · Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Cambridge, United Kingdom · Faculty Computer Science and Applied Mathematics, University of Applied Sciences Technikum Wien, Vienna, Austria · Department of Radiation Oncology, University Hospital Vienna and Medical University of Vienna, Vienna, Austria · Department of Radiology, Montpellier Cancer Institute, PINKCC lab U1194, University of Montpellier, Montpellier, France · Department of Radiology, Faculty of Medicine, Urmia University of Medical Science, Urmia, Iran · Clinical Institute for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Wiener Neustadt, Wiener Neustadt, Austria · Clinical Department of Ophthalmology and Optometry, University Hospital Wiener Neustadt, Wiener Neustadt, Austria · Image X Institute, Faculty of Medicine and Health, University of Sydney, Australia
Abstract
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.