一种新型冠状病毒感染肺部CT影像的超分辨率重建方法

By employing an image super-resolution method based on generative adversarial networks, the problem of low resolution in CT images of the lungs infected with the novel coronavirus was addressed. This method improves image resolution and enhances the texture details of lung CT images while shortening scan time, thus supporting more accurate diagnosis and treatment.

CN116452691BActive Publication Date: 2026-07-17UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
UNIV OF ELECTRONICS SCI & TECH OF CHINA
Filing Date
2023-04-19
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

The low resolution of CT images of the lungs infected with the novel coronavirus affects doctors' judgment of the patient's condition.

Method used

A generative adversarial network (GAN)-based image super-resolution method is established. The GAN is trained using an image degradation model and a dataset to achieve super-resolution reconstruction of CT images.

Benefits of technology

While reducing scan time, CT image resolution is improved, and lung texture details are enhanced, helping doctors to make better diagnoses and treatments.

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Abstract

本发明公开了一种新型冠状病毒感染肺部CT影像的超分辨率重建方法，包括利用肺部CT影像退化模型，建立超分网络训练数据集；构建基于生成对抗网络的图像超分辨率模型，模型包含浅层特征提取模块，多支路深层特征提取模块以及图像重建模块。通过该方法以低投影角度的新型冠状病毒感染肺部CT影像为基础，利用生成对抗网络实现CT影像的超分辨率重建，强化图像中的细节和纹理信息，辅助医护人员更快地对新型冠状病毒感染患者进行筛查和病情评估。
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