基于 Res-U-Net GANs 的创新型金属伪影降低算法。
An Innovative Metal Artifact Reduction Algorithm based on Res-U-Net GANs.
机构信息
Hefei Institutes of Physical Science, Chinese Academy of Sciences, Box: 230031, Hefei, China.
University of Science and Technology of China, Box: 230026, Hefei, China.
出版信息
Curr Med Imaging. 2023;19(13):1549-1560. doi: 10.2174/1573405619666230217102534.
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.
背景
在 X 射线计算机断层扫描（CT）中，病人体内的金属植入物会产生严重的伪影，降低图像质量并干扰医生的判断。因此，有必要开发一种用于去除 CT 图像中金属伪影并重建高质量图像的算法。
目的
在本文中，我们提出了一种基于生成对抗网络（GAN）的图像域金属伪影减少算法，即 Res-U-Net GANs。该方法可以有效抑制噪声并去除 CT 图像中的金属伪影。
方法
我们的新方法包括生成器和鉴别器。生成器包含几个残差块、U-Net 结构和跳跃连接。同时还使用了加权联合损失函数进行训练。这些结构可以减少图像中的金属伪影，提高图像质量并恢复植入物的细节。
结果
我们使用 SSIM、PSNR 和 RMSE 来评估所提出方法的性能。测试集图像的平均 SSIM、PSNR 和 RMSE 分别为 0.977、39.044 和 0.011。编译和封装的训练模型在处理临床数据集时也表现出了优异的性能，可以去除临床 CT 图像中的金属伪影。
结论
我们认为所提出的算法可以去除 CT 图像中的金属伪影并恢复图像细节，这对放射科医生非常有帮助。
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