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A deep learning-based ring artifact correction method for X-ray CT

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Abstract

Purpose

In X-ray CT systems, ring artifacts caused by the nonuniform response of detector elements degrades the reconstruction quality and affects the subsequent processing and quantitative analysis of the image.

Method

In this paper, a novel method is proposed to remove the ring artifacts in CT image by applying deep learning algorithm based on convolutional neural network (CNN) and recurrent neural network (RNN). First, the reconstructed CT images is transformed into polar coordinate system to make rings appear as stripes. Then, a CNN is constructed to detect the stripes, and a RNN is utilized to process the line artifact correction. After that, by retransforming the corrected image from polar coordinate system to Cartesian coordinate system, a ring artifact removal image can be achieved.

Results

The presented method can successfully reduce the CT ring artifact on simulated and real data. Specifically, in the experiment with real water phantom, the center and peripheral standard deviations reduced 46% and 24%, respectively.

Conclusions

The proposed method is potential to be widely deployed in industrial and medical CT systems, due to the excellent results on correction and the real-time performance without adjusting parameters manually.

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Acknowledgements

This work is partially supported by National Key R&D Program of China (No. 2017YFF0107201) and CAS Interdisciplinary Innovation Team (No. JCTD-2019-02), National Natural Science Foundation of China (NSFC) (No. 11975250), the Science and Technology Service Network Initiative of Chinese Academy of Sciences (No. KFJ-STS-QYZD-193) and the Key Technology Research and Development Team Project of Chinese Academy of Sciences (No. GJJSTD20200004).

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Authors and Affiliations

  1. Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China

    Lulu Yuan, Qiong Xu, Baodong Liu, Zhe Wang, Shuangquan Liu, Cunfeng Wei & Long Wei

  2. School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China

    Lulu Yuan, Baodong Liu, Cunfeng Wei & Long Wei

Authors
  1. Lulu Yuan
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  2. Qiong Xu
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  3. Baodong Liu
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  4. Zhe Wang
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  5. Shuangquan Liu
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  6. Cunfeng Wei
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  7. Long Wei
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Corresponding author

Correspondence to Cunfeng Wei.

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The authors declared that there is no conflict of interest.

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Yuan, L., Xu, Q., Liu, B. et al. A deep learning-based ring artifact correction method for X-ray CT. Radiat Detect Technol Methods 5, 493–503 (2021). https://doi.org/10.1007/s41605-021-00286-1

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  • DOI: https://doi.org/10.1007/s41605-021-00286-1

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