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Motion artifact removal in coronary CT angiography based on generative adversarial networks

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Coronary motion artifacts affect the diagnostic accuracy of coronary CT angiography (CCTA), especially in the mid right coronary artery (mRCA). The purpose is to correct CCTA motion artifacts of the mRCA using a GAN (generative adversarial network).

Methods

We included 313 patients with CCTA scans, who had paired motion-affected and motion-free reference images at different R-R interval phases in the same cardiac cycle and included another 53 CCTA cases with invasive coronary angiography (ICA) comparison. Pix2pix, an image-to-image conversion GAN, was trained by the motion-affected and motion-free reference pairs to generate motion-free images from the motion-affected images. Peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Dice similarity coefficient (DSC), and Hausdorff distance (HD) were calculated to evaluate the image quality of GAN-generated images.

Results

At the image level, the median of PSNR, SSIM, DSC, and HD of GAN-generated images were 26.1 (interquartile: 24.4–27.5), 0.860 (0.830–0.882), 0.783 (0.714–0.825), and 4.47 (3.00–4.47), respectively, significantly better than the motion-affected images (p < 0.001). At the patient level, the image quality results were similar. GAN-generated images improved the motion artifact alleviation score (4 vs. 1, p < 0.001) and overall image quality score (4 vs. 1, p < 0.001) than those of the motion-affected images. In patients with ICA comparison, GAN-generated images achieved accuracy of 81%, 85%, and 70% in identifying no, < 50%, and ≥ 50% stenosis, respectively, higher than 66%, 72%, and 68% for the motion-affected images.

Conclusion

Generative adversarial network-generated CCTA images greatly improved the image quality and diagnostic accuracy compared to motion-affected images.

Key Points

• A generative adversarial network greatly reduced motion artifacts in coronary CT angiography and improved image quality.

• GAN-generated images improved diagnosis accuracy of identifying no, < 50%, and ≥ 50% stenosis.

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Abbreviations

CCTA:

Coronary computed tomography angiography

CNN:

Convolutional neural network

DSC:

Dice similarity coefficient

GAN:

Generative adversarial network

HD:

Hausdorff distance

HR:

Heart rate

mRCA:

the Middle segment of right coronary artery

PSNR:

Peak signal-noise ratio

RCA:

Right coronary artery

SSIM:

Structural similarity

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Funding

This study has received funding from the National Natural Science Foundation of China (81971612 and 81471662), the Ministry of Science and Technology of China (2016YFE0103000), and Shanghai Jiao Tong University (ZH2018ZDB10).

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Authors and Affiliations

  1. Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China

    Lu Zhang, Beibei Jiang, Lingyun Wang, Keke Zhao, Yaping Zhang & Xueqian Xie

  2. Shukun (Beijing) Technology Co, Ltd., Jinhui Bd, Qiyang Rd, Beijing, 100102, China

    Qiang Chen

  3. Department of Radiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700RB, Groningen, The Netherlands

    Rozemarijn Vliegenthart

Authors
  1. Lu Zhang
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  2. Beibei Jiang
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  3. Qiang Chen
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  4. Lingyun Wang
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  5. Keke Zhao
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  6. Yaping Zhang
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  7. Rozemarijn Vliegenthart
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  8. Xueqian Xie
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Corresponding author

Correspondence to Xueqian Xie.

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Guarantor

The scientific guarantor of this publication is Xueqian Xie.

Conflict of interest

Dr. Qiang Chen is an employee of Shukun (Beijing) Technology Co, Ltd., who provided technical consultation but had no control over any data and information that may cause conflicts of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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• retrospective

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• performed at one institution

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Zhang, L., Jiang, B., Chen, Q. et al. Motion artifact removal in coronary CT angiography based on generative adversarial networks. Eur Radiol 33, 43–53 (2023). https://doi.org/10.1007/s00330-022-08971-5

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  • DOI: https://doi.org/10.1007/s00330-022-08971-5

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