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

• URL: http://arxiv.org/abs/2607.29253v1
• Date: Fri, 31 Jul 2026 10:24:20 GMT
• Title: CBCT-IQ: A Publicly Available Annotated Cone-Beam CT Dataset for Image Quality Assessment and Benchmarking
• Abstract summary: We provide the first open-access CBCT IQA dataset containing 1,764 annotated image slices.<n>We benchmark 26 full reference- and no reference-based IQA measures against expert annotations and introduce an exploratory IQA measure-based ranking.
• Score: 13.49893413630687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• FunPiQ: A New Benchmark for Pixel-Level Quality Assessment in Fundus Images [2.3155109250337182]
  We introduce FunPiQ, the first FIQA benchmark to provide pixel-level quality annotations.<n>We propose EFIQA-CP, an explainable-by-design (EBD) method that uses quality pseudo-labels based on anatomical visibility to train a CNN via Non-Negative Positive-Unlabeled learning.
  arXiv  Detail & Related papers  (2026-06-24T14:59:34Z)
• CAP-IQA: Context-Aware Prompt-Guided CT Image Quality Assessment [4.6321193613229825]
  We propose the Context-Aware Prompt-guided Image Quality Assessment framework.<n>It integrates text-level priors with instance-level context prompts.<n>It applies causal debiasing to separate idealized knowledge from factual, image-specific degradations.
  arXiv  Detail & Related papers  (2026-01-04T17:30:45Z)
• PhotIQA: A photoacoustic image data set with image quality ratings [7.753621023890248]
  PhotIQA is a data set consisting of 1134 reconstructed photoacoustic (PA) images rated by 2 experts across five quality properties.<n>Our baseline experiments show that HaarPSI$_med$ significantly outperforms SSIM in correlating with the quality ratings.
  arXiv  Detail & Related papers  (2025-07-04T11:06:54Z)
• CAS-IQA: Teaching Vision-Language Models for Synthetic Angiography Quality Assessment [11.527245046470123]
  Low-quality synthetic angiographies can significantly increase procedural risk.<n>This paper proposes CAS-IQA, a vision-language model (VLM)-based framework that predicts fine-grained quality scores.<n>Experiments on the CAS-3K dataset demonstrate that CAS-IQA significantly outperforms state-of-the-art IQA methods by a considerable margin.
  arXiv  Detail & Related papers  (2025-05-23T08:27:05Z)
• Metrics that matter: Evaluating image quality metrics for medical image generation [48.85783422900129]
  This study comprehensively assesses commonly used no-reference image quality metrics using brain MRI data.<n>We evaluate metric sensitivity to a range of challenges, including noise, distribution shifts, and, critically, morphological alterations designed to mimic clinically relevant inaccuracies.
  arXiv  Detail & Related papers  (2025-05-12T01:57:25Z)
• DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild [73.6767681305851]
  Blind image quality assessment (IQA) in the wild presents significant challenges.<n>Given the difficulty in collecting large-scale training data, leveraging limited data to develop a model with strong generalization remains an open problem.<n>Motivated by the robust image perception capabilities of pre-trained text-to-image (T2I) diffusion models, we propose a novel IQA method, diffusion priors-based IQA.
  arXiv  Detail & Related papers  (2024-05-30T12:32:35Z)
• When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents [92.45867913876691]
  No-reference image quality assessment (NR-IQA) models can effectively quantify perceived image quality.<n>We show that NR-IQA models can be plugged into the maximum a posteriori (MAP) estimation framework for image enhancement.
  arXiv  Detail & Related papers  (2024-03-11T03:35:41Z)
• Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment [58.09173822651016]
  We argue that it is possible to learn reference knowledge under the No-Reference Image Quality Assessment setting. We propose a new framework to learn comparative knowledge from non-aligned reference images. Experiments on eight standard NR-IQA datasets demonstrate the superior performance to the state-of-the-art NR-IQA methods.
  arXiv  Detail & Related papers  (2023-12-01T13:56:01Z)
• Blind Multimodal Quality Assessment: A Brief Survey and A Case Study of Low-light Images [73.27643795557778]
  Blind image quality assessment (BIQA) aims at automatically and accurately forecasting objective scores for visual signals. Recent developments in this field are dominated by unimodal solutions inconsistent with human subjective rating patterns. We present a unique blind multimodal quality assessment (BMQA) of low-light images from subjective evaluation to objective score.
  arXiv  Detail & Related papers  (2023-03-18T09:04:55Z)
• UNO-QA: An Unsupervised Anomaly-Aware Framework with Test-Time Clustering for OCTA Image Quality Assessment [4.901218498977952]
  We propose an unsupervised anomaly-aware framework with test-time clustering for optical coherence tomography angiography ( OCTA) image quality assessment. A feature-embedding-based low-quality representation module is proposed to quantify the quality of OCTA images. We perform dimension reduction and clustering of multi-scale image features extracted by the trained OCTA quality representation network.
  arXiv  Detail & Related papers  (2022-12-20T18:48:04Z)
• Confusing Image Quality Assessment: Towards Better Augmented Reality Experience [96.29124666702566]
  We consider AR technology as the superimposition of virtual scenes and real scenes, and introduce visual confusion as its basic theory. A ConFusing Image Quality Assessment (CFIQA) database is established, which includes 600 reference images and 300 distorted images generated by mixing reference images in pairs. An objective metric termed CFIQA is also proposed to better evaluate the confusing image quality.
  arXiv  Detail & Related papers  (2022-04-11T07:03:06Z)
• Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment [157.1292674649519]
  We propose a practical solution named degraded-reference IQA (DR-IQA) DR-IQA exploits the inputs of IR models, degraded images, as references. Our results can even be close to the performance of full-reference settings.
  arXiv  Detail & Related papers  (2021-08-18T02:35:08Z)
• Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild [98.48284827503409]
  We develop a textitunified BIQA model and an approach of training it for both synthetic and realistic distortions. We employ the fidelity loss to optimize a deep neural network for BIQA over a large number of such image pairs. Experiments on six IQA databases show the promise of the learned method in blindly assessing image quality in the laboratory and wild.
  arXiv  Detail & Related papers  (2020-05-28T13:35:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.