CT-DegradBench: A Physics-Informed Benchmark for CT Degradation Detection and Severity Estimation

• URL: http://arxiv.org/abs/2605.16431v1
• Date: Thu, 14 May 2026 19:30:00 GMT
• Title: CT-DegradBench: A Physics-Informed Benchmark for CT Degradation Detection and Severity Estimation
• Abstract summary: We present CT-DegradBench, a dataset and benchmark for CT degradation detection and severity estimation.<n>We propose SeSpeCT, a framework that combines semantic priors from medical vision-language models with complementary frequency-domain cues for artifact analysis.
• Score: 42.85377247586073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computed tomography (CT) images are frequently degraded by acquisition artifacts, including noise, blur, streaking, aliasing, and metal artifacts. Yet CT enhancement is still largely evaluated using image quality metrics with limited perceptual and clinical validity, while existing datasets remain focused on isolated restoration tasks, hindering unified benchmarking across diverse degradation types. We present CT-DegradBench, a dataset and benchmark for CT degradation detection and severity estimation under controlled single- and mixed-artifact settings. CT-DegradBench enables systematic evaluation across multiple degradation families and severity levels within a common experimental framework. We further propose SeSpeCT (Semantic-Spectral CT degradation estimation), a framework that combines semantic priors from medical vision-language models with complementary frequency-domain cues for artifact analysis. SeSpeCT constructs a training-free semantic quality axis in the multimodal embedding space using radiology-informed text prompts, without task-specific fine-tuning, and combines it with spectral features that capture degradation-specific frequency patterns. The resulting representation enables joint prediction of artifact type and severity. Experimental results show that SeSpeCT consistently outperforms the evaluated baselines under both single- and mixed-degradation settings. The framework is available at https://github.com/yousranb/CT-DEGRADBENCH.

Related papers

• Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts [56.74856091904914]
  We propose a unified framework combining parameter-informed contrast disentanglement with severity-aware adaptive correction.<n>ScanCLIP derives contrast embeddings from acquisition parameters to disentangle contrast style from anatomical content, yielding contrast-free features.<n>A Vision Transformer then estimates motion severity and routes features through a Mixture-of-Experts network, enabling targeted artifact correction.
  arXiv  Detail & Related papers  (2026-05-29T02:48:23Z)
• Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling [51.330452704464925]
  We train a latent diffusion model on 1,046 PCCTs and 405,379 EICTs from 145 hospitals.<n>We construct a large-scale dataset of over 17,316 publicly available EICTs enhanced to PCCT-like quality.<n>Our results suggest emerging imaging advances can be systematically distilled into routine EICT using limited high-quality scans as reference.
  arXiv  Detail & Related papers  (2026-04-08T17:47:35Z)
• CT-Bench: A Benchmark for Multimodal Lesion Understanding in Computed Tomography [16.912129516334783]
  CT-Bench is a first-of-its-kind benchmark dataset for lesion analysis on computed tomography (CT)<n>It consists of a Lesion Image and Metadata Set containing 20,335 lesions from 7,795 CT studies with bounding boxes, descriptions, and size information, and a visual question answering benchmark with 2,850 QA pairs covering lesion localization, description, size estimation, and attribute categorization.<n>We evaluate state-of-the-art multimodal models, including vision-language and medical CLIP variants, by comparing their performance to radiologist assessments.
  arXiv  Detail & Related papers  (2026-02-16T16:10:19Z)
• Multi-Stage Residual-Aware Unsupervised Deep Learning Framework for Consistent Ultrasound Strain Elastography [0.9698885636466451]
  Ultrasound Strain Elastography (USE) is a powerful non-invasive imaging technique for assessing tissue mechanical properties.<n>We propose MUSSE-Net, a residual-aware, multi-stage unsupervised sequential deep learning framework for robust and consistent strain estimation.<n> MUSSE-Net achieves state-of-the-art performance with a target SNR of 24.54, background SNR of 132.76, CNR of 59.81, and elastographic SNR of 9.73 on simulation data.
  arXiv  Detail & Related papers  (2025-11-19T17:22:25Z)
• EqDiff-CT: Equivariant Conditional Diffusion model for CT Image Synthesis from CBCT [43.92108185590778]
  Cone-beam computed tomography (CBCT) is widely used for imageguided radiotherapy (IGRT)<n>We propose a novel diffusion-based conditional generative model, coined EqDiff-CT, to synthesize high-quality CT images from CBCT.
  arXiv  Detail & Related papers  (2025-09-26T05:51:59Z)
• Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss [9.59233136691378]
  Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy. Recent deep learning-based CBCT enhancement methods have shown promising results in suppressing artifacts. We propose a novel feature-oriented deep learning framework that translates low-quality CBCT images into high-quality CT-like imaging.
  arXiv  Detail & Related papers  (2023-11-01T10:09:01Z)
• Enhanced Sharp-GAN For Histopathology Image Synthesis [63.845552349914186]
  Histopathology image synthesis aims to address the data shortage issue in training deep learning approaches for accurate cancer detection. We propose a novel approach that enhances the quality of synthetic images by using nuclei topology and contour regularization. The proposed approach outperforms Sharp-GAN in all four image quality metrics on two datasets.
  arXiv  Detail & Related papers  (2023-01-24T17:54:01Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Anatomically constrained CT image translation for heterogeneous blood vessel segmentation [3.88838725116957]
  Anatomical structures in contrast-enhanced CT (ceCT) images can be challenging to segment due to variability in contrast medium diffusion. To limit the radiation dose, generative models could be used to synthesize one modality, instead of acquiring it. CycleGAN has attracted particular attention because it alleviates the need for paired data. We present an extension of CycleGAN to generate high fidelity images, with good structural consistency.
  arXiv  Detail & Related papers  (2022-10-04T16:14:49Z)
• Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images [61.862364277007934]
  We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images. A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients. Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
  arXiv  Detail & Related papers  (2020-05-08T03:16:15Z)

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.