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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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Ring artifacts correction for computed tomography image using unsupervised contrastive learning.

Tangsheng Wang1,2, Xuan Liu1, Chulong Zhang1

  • 1Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, People's Republic of China.

Physics in Medicine and Biology
|September 15, 2023
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Summary

This study introduces a novel Dual Contrast Learning Generative Adversarial Network (DCLGAN) to effectively remove ring artifacts from computed tomography (CT) images. The DCLGAN method significantly improves CT image quality and diagnostic accuracy by preserving essential texture details.

Keywords:
CTradiation oncologyring artifactsunsupervised learning

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Artificial Intelligence

Background:

  • Computed tomography (CT) is crucial for disease detection but often degraded by ring artifacts.
  • These artifacts, caused by hardware issues, impair image quality and hinder accurate diagnosis.

Purpose of the Study:

  • To develop and validate a novel method, the Dual Contrast Learning Generative Adversarial Network (DCLGAN), for effective ring artifact removal in CT images.
  • To preserve crucial image texture details while eliminating artifacts.

Main Methods:

  • Simulated ring artifacts on real CT data to create uncorrected CT (uCT) data, transforming them into strip artifacts.
  • Applied DCLGAN in polar coordinates to remove strip artifacts, generating synthetic CT (sCT) images.
  • Calculated residual images, extracted ring artifacts via inverse polar transformation, and subtracted them from original CT images for correction.

Main Results:

  • Demonstrated significant artifact reduction on real CT, simulated, and cone beam CT brain images.
  • Achieved a 12.36 HU reduction in mean absolute error and 18.94 HU decrease in root mean square error.
  • Improved peak signal-to-noise ratio by 3.53 dB and structural similarity index by 9.24%.

Conclusions:

  • The DCLGAN method effectively eliminates ring artifacts from CT images.
  • The approach successfully preserves fine texture details, enhancing diagnostic utility.
  • This technique offers a valuable advancement for CT imaging quality and clinical applications.