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A new iterative algorithm for ring artifact reduction in CT using ring total variation.

Morteza Salehjahromi1, Qian Wang1, Yanbo Zhang2

  • 1Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA.

Medical Physics
|August 14, 2019
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Summary
This summary is machine-generated.

This study introduces a new optimization model to eliminate ring artifacts in CT images caused by detector issues. The method effectively reduces artifacts, improving image quality in both simulated and real-world scans.

Keywords:
alternating direction method of multipliersalternating minimization schemecomputed tomographyiterative reconstructionring artifactring total variation

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Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Computational Imaging

Background:

  • Detector element imperfections in computed tomography (CT) cause stripe artifacts in sinograms.
  • These sinogram artifacts manifest as concentric ring artifacts in reconstructed CT images.
  • Existing methods struggle with comprehensive ring artifact removal, impacting diagnostic accuracy.

Purpose of the Study:

  • To propose a novel optimization model for iterative CT image reconstruction.
  • To effectively remove ring artifacts originating from detector miscalibration.
  • To simultaneously correct sinogram data and refine reconstructed images.

Main Methods:

  • Developed a Ring Total Variation (RTV) regularization to penalize ring artifacts in the image domain.
  • Introduced a correcting vector to compensate for detector malfunctions in the projection (sinogram) domain.
  • Employed an alternating minimization scheme (AMS) with ADMM for iterative image and sinogram updates.

Main Results:

  • Evaluated using simulated datasets (Shepp-Logan, chest scan, noisy low-contrast phantom) and a physical phantom.
  • Demonstrated superior performance over wavelet-Fourier filtering, Brun et al., and Paleo and Mirone methods.
  • Achieved significant improvements in quantitative metrics (RMSE, SSIM) and qualitative visual assessment.

Conclusions:

  • The proposed RTV-based optimization model effectively reduces ring artifacts in CT images.
  • Achieved state-of-the-art performance in both simulated and physical phantom studies.
  • The method enhances image quality, evidenced by improved RMSE, SSIM, and visual fidelity.