CBCT-IQ: A Publicly Available Annotated Cone-Beam CT Dataset for Image Quality Assessment and Benchmarking

2026-07-31

AI Summary

This study addresses the lack of publicly available, expert-annotated datasets that has hindered research on cone-beam computed tomography (CBCT) image quality assessment (IQA). To bridge this gap, we introduce CBCT-IQA, the first open-access dataset comprising 1,764 image slices systematically generated by varying acquisition and reconstruction parameters, each annotated with quality scores by three clinical experts using a four-point scale. Leveraging this dataset, we conduct a comprehensive benchmark evaluation of 26 full-reference and no-reference IQA metrics and propose an IQA-driven ranking method capable of discerning subtle quality differences. This work establishes a reproducible, standardized benchmark and provides a valuable public resource to advance CBCT image quality evaluation.

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.

Problem

• CBCT
• image quality assessment
• annotated dataset
• benchmarking
• medical imaging

Innovation

• CBCT
• image quality assessment
• public dataset
• benchmarking
• expert annotation