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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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Quantifying Intermembrane Distances with Serial Image Dilations
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Computed tomography ring artifact correction method with super-pixel segmentation and adaptive relative total

Na Li1, Yingchun Yin2, Junxiong Zhao3

  • 1Department of Biomedical Engineering, Guangdong Medical University, Dongguan, China.

Quantitative Imaging in Medicine and Surgery
|April 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-stage method to remove ring artifacts in computed tomography (CT) images, enhancing diagnostic accuracy. The technique effectively corrects both high and low-intensity artifacts, preserving image quality for photon counting CT (PCCT).

Keywords:
Computed tomography (CT)ring artifactsuper-pixel segmentationtotal variation

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Photon Counting CT (PCCT)

Background:

  • Ring artifacts are a persistent problem in computed tomography (CT) imaging, often caused by hardware inconsistencies.
  • These artifacts degrade image quality, impacting diagnostic accuracy and clinical utility.
  • Photon counting CT (PCCT) presents unique challenges for artifact removal due to its advanced technology.

Purpose of the Study:

  • To develop and validate a comprehensive method for removing ring artifacts in CT images.
  • To specifically address the challenges of artifact correction in photon counting CT (PCCT).
  • To preserve essential image details and grayscale balance during artifact removal.

Main Methods:

  • A two-stage artifact correction approach combining super-pixel segmentation and adaptive Relative Total Variation (RTV).
  • Super-pixel segmentation is used for high-intensity artifact removal.
  • Adaptive RTV, optimized by mesh adaptive direct search, addresses low-intensity artifacts.

Main Results:

  • Significant reduction in both high and low-intensity ring artifacts was observed.
  • The method successfully maintained the structural integrity and grayscale balance of CT images.
  • The proposed technique demonstrated versatility and robustness across various imaging scenarios and equipment.

Conclusions:

  • The developed method effectively removes ring artifacts in CT images, particularly for PCCT.
  • This advancement improves image quality and supports new applications of PCCT in precision medicine.
  • The study provides publicly available code for reproducibility.