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Metal artifact reduction for oral and maxillofacial computed tomography images by a generative adversarial network

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Abstract

Metal artifacts in oral and maxillofacial computed tomography (CT) images affect the imaging quality, interfere with doctors’ judgment of anatomical structure and may pose a threat to patients’ lives. Although many metal artifact reduction (MAR) methods have been proposed, there is still no complete and ideal solution. We aimed to construct and validate a generative adversarial network (GAN) based model, which was called MAR-GAN, to reduce the artifacts and improve the image quality. We created a large CT images dataset containing 600 pairs of simulated images and 5203 clinical images, where five kinds of metal artifacts were involved. The 600 pairs of simulated images were divided into training and test groups (4:1) and tested using a 5-fold cross-validation schema. Each pair contained an original artifact-free image and a manually simulated artifact-affected image. The inputs were artifact-affected images, and the outputs were artifact-reduced images that were compared with the corresponding artifact-free images. The effectiveness of the proposed MAR-GAN was tested on the simulated dataset and the clinical dataset. The root mean square error and structural similarity values of MAR-GAN were 0.0170 ± 0.0049 and 0.9831 ± 0.0073, respectively, on the simulated dataset. MAR-GAN had significant advantages over two state-of-the-art methods on the clinical dataset, good performance was obtained and the results were all clinically acceptable. Experimental results demonstrated the superior capability of MAR-GAN, which provided improved performance and high reconstruction quality, including preservation of anatomical structures near metal implants and recovery of detailed structural information from low-quality images.

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Notes

  1. Code is available at https://github.com/LeiXuSCU/MAR-GAN.git.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China under Grant 2018AAA0100201 and the Regional Innovation Cooperation Project of Sichuan Province under Grant 2020YFQ0012. We also thank Richard Lipkin, PhD, from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript. L. Xu, J.X. Guo. and Z. Yi designed the study, developed algorithms, conducted the experiments and wrote the manuscript. S.L Zhou, W.D. Tian and W. Tang collected and processed the data, evaluated and scored the experimental results on the clinical dataset. All authors read and approved the final version of the manuscript.

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Authors and Affiliations

  1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu, 610065, People’s Republic of China

    Lei Xu, Jixiang Guo & Zhang Yi

  2. Department of Oral and Maxillofacial Surgery, West China College of Stomatology, Sichuan University, No. 14, 3rd Section, Renmin South Rd, Chengdu, 610041, People’s Republic of China

    Shanluo Zhou, Weidong Tian & Wei Tang

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  1. Lei Xu
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  2. Shanluo Zhou
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  5. Wei Tang
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  6. Zhang Yi
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Correspondence to Wei Tang or Zhang Yi.

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Xu, L., Zhou, S., Guo, J. et al. Metal artifact reduction for oral and maxillofacial computed tomography images by a generative adversarial network. Appl Intell 52, 13184–13194 (2022). https://doi.org/10.1007/s10489-021-02905-2

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