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Sparsity-based method for ring artifact elimination in computed tomography.

Mona Selim^1,2, Essam A Rashed³, Mohammed A Atiea¹

¹Department of Computer Science, Faculty of Computers and Information, Suez University, Suez, Egypt.

|June 28, 2022

Summary

This summary is machine-generated.

This study introduces a novel computed tomography (CT) method using compressed sensing to eliminate ring artifacts. The technique effectively removes artifact-causing errors by minimizing sparse error components, improving image reconstruction quality.

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Area of Science:

Medical Imaging
Image Processing
Computational Science

Background:

Ring artifacts are a common issue in computed tomography (CT) reconstructed images, appearing as concentric circles.
These artifacts degrade image quality and can obscure diagnostic information.

Purpose of the Study:

To propose and evaluate a novel method for eliminating ring artifacts during CT image reconstruction.
To leverage compressed sensing theory for artifact reduction.

Main Methods:

Representing projection data as a sum of ideal and error components.
Minimizing sparsity-induced norms of the error components to eliminate ring artifacts.
Utilizing an iterative algorithm based on the alternating direction method of multipliers (ADMM).
Investigating different sparse models and incorporating smoothing penalty functions and angular constraints.

Main Results:

The proposed method effectively eliminates ring artifacts.
Improved algorithms incorporating smoothing penalties and angular constraints demonstrate enhanced performance.
Real data and simulation studies validate the effectiveness of the proposed techniques.

Conclusions:

The developed compressed sensing-based approach offers an effective solution for ring artifact elimination in CT.
The enhanced algorithms provide superior artifact correction, leading to improved image quality for diagnostic purposes.