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Complete Ring Artifacts Reduction Procedure for Lab-Based X-ray Nano CT Systems.

Jakub Šalplachta¹, Tomáš Zikmund¹, Marek Zemek¹

¹CEITEC-Central European Institute of Technology, Brno University of Technology, Purkyňova 656/123, 612 00 Brno, Czech Republic.

Sensors (Basel, Switzerland)

|January 6, 2021

Summary

This study presents a new computed tomography (CT) ring artifact reduction method that effectively removes artifacts by differentiating and correcting them in the sinogram domain. The robust approach enhances image quality while preserving crucial sample details.

Keywords:

CCD detector high-resolution X-ray computed tomography relative total variation ring artifacts reduction sCMOS detector

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Area of Science:

Medical Imaging
Image Processing
Computational Science

Background:

Ring artifacts are a common issue in computed tomography (CT) imaging, degrading image quality and potentially leading to misinterpretations.
Existing methods for ring artifact reduction have limitations and individual weaknesses that necessitate a more comprehensive approach.

Purpose of the Study:

To introduce a novel, robust procedure for reducing ring artifacts in CT data.
To differentiate and address two types of ring artifacts based on their cause and characteristics.

Main Methods:

Developed a combined approach integrating ideas from existing methods for enhanced artifact reduction.
Utilized novel iterative schemes based on relative total variations (RTV) for artifact detection.
Employed image inpainting and intensity deviation smoothing for artifact correction in the sinogram domain.

Main Results:

The procedure was successfully implemented and tested on lab-based X-ray nano CT data (CCD and sCMOS) and simulated data.
Quantitative evaluation demonstrated high efficiency in ring removal and accurate artifact detection.
The method effectively preserved the spatial resolution and original structure of the scanned samples.

Conclusions:

The proposed ring artifact reduction procedure offers a robust and effective solution for improving CT image quality.
The method's ability to differentiate and correct artifact types separately enhances its performance over existing techniques.
This approach is valuable for applications requiring high-fidelity CT imaging, such as in materials science and non-destructive testing.