Share Email Print
cover

Proceedings Paper

Wasserstein generative adversarial networks for motion artifact removal in dental CT imaging
Author(s): Changhui Jiang; Qiyang Zhang; Yongshuai Ge; Dong Liang; Yongfeng Yang; Xin Liu; Hairong Zheng; Zhanli Hu
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

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.

Paper Details

Date Published: 1 March 2019
PDF: 8 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094836 (1 March 2019); doi: 10.1117/12.2511818
Show Author Affiliations
Changhui Jiang, Shenzhen Institutes of Advanced Technology (China)
Shenzhen College of Advanced Technology (China)
Qiyang Zhang, Shenzhen Institutes of Advanced Technology (China)
Shenzhen College of Advanced Technology (China)
Yongshuai Ge, Shenzhen Institutes of Advanced Technology (China)
Dong Liang, Shenzhen Institutes of Advanced Technology (China)
Yongfeng Yang, Shenzhen Institutes of Advanced Technology (China)
Xin Liu, Shenzhen Institutes of Advanced Technology (China)
Hairong Zheng, Shenzhen Institutes of Advanced Technology (China)
Zhanli Hu, Shenzhen Institutes of Advanced Technology (China)


Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

© SPIE. Terms of Use
Back to Top