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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Research Article

An Innovative Metal Artifact Reduction Algorithm based on Res-U-Net GANs

Author(s): Ziheng Zhang, Minghan Yang, Lei Xu*, Jiazhao Yang, Hu Guo and Jianye Wang

Volume 19, Issue 13, 2023

Published on: 20 March, 2023

Article ID: e170223213750 Pages: 12

DOI: 10.2174/1573405619666230217102534

Price: $65

Abstract

Background: During X-ray computed tomography (CT) scans, the metal implants in the patient's body will produce severe artifacts, which reduce the image quality and interferes with the doctor's judgment. Therefore, it is necessary to develop an algorithm for removing metal artifacts in CT images and reconstructing high-quality images.

Objective: In this article, we proposed a generative adversarial networks (GANs)-based metal artifact reduction algorithm for the image domain, Res-U-Net GANs. This method can effectively suppress noise and remove metal artifacts in CT images.

Methods: Our new approach includes a generator and a discriminator. The generator contains several residual blocks, a U-Net structure and skip connections. And a weighted joint loss function is also used for training. These structures can reduce metal artifacts in images, improve image quality, and restore implant details.

Results: We use SSIM, PSNR and RMSE to evaluate the performance of the proposed method. The mean SSIM, PSNR and RMSE of the testing set images are 0.977, 39.044 and 0.011, respectively. And the trained model which is compiled and encapsulated, also show excellent performance in processing clinical data sets, which can remove metal artifacts in clinical CT images.

Conclusion: We consider that the proposed algorithm can remove metal artifacts in CT images and restore image details, which is very helpful for radiologists.

Graphical Abstract

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