Dr. Zhanli Hu Profile

Dr. Zhanli Hu

at Shenzhen Institutes of Advanced Technology

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Proceedings Article | 1 March 2019 Paper

Wasserstein generative adversarial networks for motion artifact removal in dental CT imaging

Changhui Jiang, Qiyang Zhang, Yongshuai Ge, Dong Liang, Yongfeng Yang, Xin Liu, Hairong Zheng, Zhanli Hu

Proceedings Volume 10948, 1094836 (2019) https://doi.org/10.1117/12.2511818

KEYWORDS: Computed tomography, Gallium nitride, X-ray computed tomography, Image quality, Neural networks, Head, Image quality standards, Biomedical optics, Data acquisition, Algorithm development

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In dental computed tomography (CT) scanning, high-quality images are crucial for oral disease diagnosis and treatment. However, many artifacts, such as metal artifacts, downsampling artifacts and motion artifacts, can degrade the image quality in practice. The main purpose of this article is to reduce motion artifacts. Motion artifacts are caused by the movement of patients during data acquisition during the dental CT scanning process. To remove motion artifacts, the goal of this study was to develop a dental CT motion artifact-correction algorithm based on a deep learning approach. We used dental CT data with motion artifacts reconstructed by conventional filtered back-projection (FBP) as inputs to a deep neural network and used the corresponding high-quality CT data as labeled data during training. We proposed training a generative adversarial network (GAN) with Wasserstein distance and mean squared error (MSE) loss to remove motion artifacts and to obtain high-quality CT dental images. In our network, to improve the generator structure, the generator used a cascaded CNN-Net style network with residual blocks. To the best of our knowledge, this work describes the first deep learning architecture method used with a commercial cone-beam dental CT scanner. We compared the performance of a general GAN and the m-WGAN. The experimental results confirmed that the proposed algorithm effectively removes motion artifacts from dental CT scans. The proposed m-WGAN method resulted in a higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) and a lower root-mean-squared error (RMSE) than the general GAN method.

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