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Related Experiment Video

Updated: Mar 13, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Visual language model-assisted CT denoising via text-guided diffusion and fidelity maintenance.

Ye Shen¹, Ningning Liang¹, Ailong Cai¹

¹Department of Henan Key Laboratory of Imaging and Intelligent Processing, Information Engineering University, Zhengzhou, China.

Journal of X-Ray Science and Technology

|March 12, 2026

Summary

A new Visual-Language Model-assisted CT Denoising (VLD) framework reduces noise in computed tomography (CT) scans without needing paired data. This method improves image quality and diagnostic fidelity for safer patient imaging.

Keywords:

computed tomography diffusion models image denoising prompt engineering visual language model

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Area of Science:

Medical Imaging
Artificial Intelligence
Image Processing

Background:

Reducing radiation dose in computed tomography (CT) and photon-counting CT (PCCT) is vital for patient safety.
Low-dose CT imaging introduces noise, degrading image quality and potentially impacting diagnostic accuracy.
Current denoising methods often require paired data or rely on specific noise assumptions, limiting clinical applicability.

Purpose of the Study:

To introduce a novel Visual-Language Model-assisted CT Denoising (VLD) framework for effective CT image noise reduction.
To leverage semantic understanding from visual-language models for improved CT image restoration.
To preserve diagnostic fidelity and structural integrity in low-dose CT images.

Main Methods:

Developed a Visual-Language Model-assisted CT Denoising (VLD) framework utilizing semantic guidance.
Employed a diffusion model guided by semantic understanding derived from multimodal visual-language models.
Implemented a tri-domain consistency framework for progressive refinement of image details and structural preservation.

Main Results:

The VLD method demonstrated high-quality reconstruction on simulated CT and real PCCT data.
Achieved average peak signal-to-noise ratio improvements of 0.95 dB and 1.21 dB under specific conditions.
Outperformed existing methods like WGAN and FBPConvNet, which require paired data, in simulation experiments.
Showcased robust generalization capabilities to new imaging scenarios.

Conclusions:

The VLD framework effectively reduces noise in CT images while maintaining diagnostic quality.
Leveraging visual-language models offers a promising direction for advanced medical image denoising.
The proposed method provides a robust and generalizable solution for low-dose CT denoising, overcoming limitations of previous approaches.