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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

30.06.2023 | Original Article

Two-stage generative adversarial networks for metal artifact reduction and visualization in ablation therapy of liver tumors

verfasst von: Duan Liang, Shunan Zhang, Ziqi Zhao, Guangzhi Wang, Jianqi Sun, Jun Zhao, Wentao Li, Lisa X. Xu

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 11/2023

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Abstract

Purpose

The strong metal artifacts produced by the electrode needle cause poor image quality, thus preventing physicians from observing the surgical situation during the puncture process. To address this issue, we propose a metal artifact reduction and visualization framework for CT-guided ablation therapy of liver tumors.

Methods

Our framework contains a metal artifact reduction model and an ablation therapy visualization model. A two-stage generative adversarial network is proposed to reduce the metal artifacts of intraoperative CT images and avoid image blurring. To visualize the puncture process, the axis and tip of the needle are localized, and then the needle is rebuilt in 3D space intraoperatively.

Results

Experiments show that our proposed metal artifact reduction method achieves higher SSIM (0.891) and PSNR (26.920) values than the state-of-the-art methods. The accuracy of ablation needle reconstruction is 2.76 mm average in needle tip localization and 1.64° average in needle axis localization.

Conclusion

We propose a novel metal artifact reduction and an ablation therapy visualization framework for CT-guided ablation therapy of liver cancer. The experiment results indicate that our approach can reduce metal artifacts and improve image quality. Furthermore, our proposed method demonstrates the potential for displaying the relative position of the tumor and the needle intraoperatively.

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Titel: Two-stage generative adversarial networks for metal artifact reduction and visualization in ablation therapy of liver tumors
verfasst von: Duan Liang
Shunan Zhang
Ziqi Zhao
Guangzhi Wang
Jianqi Sun
Jun Zhao
Wentao Li
Lisa X. Xu
Publikationsdatum: 30.06.2023
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 11/2023
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-023-02986-z

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Abstract

Purpose

Methods

Results

Conclusion

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