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Related Experiment Video

Updated: Aug 14, 2025

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

Detector shifting and deep learning based ring artifact correction method for low-dose CT.

Yuedong Liu^1,2, Cunfeng Wei^1,2,3, Qiong Xu^1,3

¹Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.

Medical Physics

|January 17, 2023

Summary

This summary is machine-generated.

This study introduces a novel method combining detector shifting and deep learning to simultaneously reduce noise and remove ring artifacts in low-dose CT images, improving image quality for better recognition.

Keywords:

deep learning detector random shifting low-dose CT ring artifact correction

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Area of Science:

Medical Imaging
Image Processing
Artificial Intelligence

Background:

Gain inconsistency in X-ray CT detector units causes ring artifacts, degrading image quality and hindering recognition.
Low-dose CT is crucial for reducing radiation exposure and scanning time, particularly in photon counting CT.
Simultaneous noise reduction and ring artifact suppression are essential for effective low-dose CT imaging.

Purpose of the Study:

To develop a method for simultaneous noise reduction and ring artifact removal in low-dose CT images.
To address limitations of deep learning methods in artifact suppression due to noise in low-dose CT.
To improve the feature recognition capabilities in low-dose CT images by mitigating artifacts and noise.

Main Methods:

A novel ring artifact correction method for low-dose CT utilizing detector shifting and deep learning.
Detector shifting at the projection stage transforms ring artifacts into dispersed noise.
Deep learning algorithms are employed for subsequent reduction of dispersed and statistical noise.

Main Results:

The proposed method demonstrates superior performance in removing ring artifacts from low-dose CT images compared to existing techniques.
Both simulated and real experimental data show significant improvements in Root Mean Square Errors (RMSEs) and Structural Similarity Index Measures (SSIMs).
The combined approach effectively mitigates artifacts and noise, leading to enhanced image clarity.

Conclusions:

The integrated approach of detector shifting and deep learning effectively removes ring artifacts and reduces statistical noise concurrently.
The proposed method achieves superior performance in low-dose CT image processing.
This technique offers a promising solution for improving diagnostic accuracy in low-dose CT applications.