Zhang Ziheng, Yang Minghan, Li Huijuan, Chen Shuai, Wang Jianye, Xu Lei
Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China.
University of Science and Technology of China, Hefei, Anhui, China.
J Xray Sci Technol. 2023;31(1):131-152. doi: 10.3233/XST-221260.
With the popularity of computed tomography (CT) technique, an increasing number of patients are receiving CT scans. Simultaneously, the public's attention to CT radiation dose is also increasing. How to obtain CT images suitable for clinical diagnosis while reducing the radiation dose has become the focus of researchers.
To demonstrate that limited-angle CT imaging technique can be used to acquire lower dose CT images, we propose a generative adversarial network-based image inpainting model-Low-dose imaging and Limited-angle imaging inpainting Model (LDLAIM), this method can effectively restore low-dose CT images with limited-angle imaging, which verifies that limited-angle CT imaging technique can be used to acquire low-dose CT images.
In this work, we used three datasets, including chest and abdomen dataset, head dataset and phantom dataset. They are used to synthesize low-dose and limited-angle CT images for network training. During training stage, we divide each dataset into training set, validation set and testing set according to the ratio of 8:1:1, and use the validation set to validate after finishing an epoch training, and use the testing set to test after finishing all the training. The proposed method is based on generative adversarial networks(GANs), which consists of a generator and a discriminator. The generator consists of residual blocks and encoder-decoder, and uses skip connection.
We use SSIM, PSNR and RMSE to evaluate the performance of the proposed method. In the chest and abdomen dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.984, 35.385 and 0.017, respectively. In the head dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.981, 38.664 and 0.011, respectively. In the phantom dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.977, 33.468 and 0.022, respectively. By comparing the experimental results of other algorithms in these three datasets, it can be found that the proposed method is superior to other algorithms in these indicators. Meanwhile, the proposed method also achieved the highest score in the subjective quality score.
Experimental results show that the proposed method can effectively restore CT images when both low-dose CT imaging techniques and limited-angle CT imaging techniques are used simultaneously. This work proves that the limited-angle CT imaging technique can be used to reduce the CT radiation dose, and also provides a new idea for the research of low-dose CT imaging.
随着计算机断层扫描（CT）技术的普及，越来越多的患者接受CT扫描。与此同时，公众对CT辐射剂量的关注度也在不断提高。如何在降低辐射剂量的同时获得适合临床诊断的CT图像已成为研究人员关注的焦点。
为了证明有限角度CT成像技术可用于获取低剂量CT图像，我们提出了一种基于生成对抗网络的图像修复模型——低剂量成像与有限角度成像修复模型（LDLAIM），该方法能够有效恢复有限角度成像的低剂量CT图像，验证了有限角度CT成像技术可用于获取低剂量CT图像。
在本研究中，我们使用了三个数据集，包括胸部和腹部数据集、头部数据集和体模数据集。它们用于合成低剂量和有限角度的CT图像以进行网络训练。在训练阶段，我们按照8:1:1的比例将每个数据集划分为训练集、验证集和测试集，在完成一个epoch训练后使用验证集进行验证，在完成所有训练后使用测试集进行测试。所提出的方法基于生成对抗网络（GAN），由一个生成器和一个判别器组成。生成器由残差块和编码器 - 解码器组成，并使用跳跃连接。
我们使用结构相似性指数（SSIM）、峰值信噪比（PSNR）和均方根误差（RMSE）来评估所提出方法的性能。在胸部和腹部数据集中，测试集的平均SSIM、PSNR和RMSE分别为0.984、35.385和0.017。在头部数据集中，测试集的平均SSIM、PSNR和RMSE分别为0.981、38.664和0.011。在体模数据集中，测试集的平均SSIM、PSNR和RMSE分别为为0.977、33.468和0.022。通过比较这三个数据集上其他算法的实验结果，可以发现所提出的方法在这些指标上优于其他算法。同时，所提出的方法在主观质量评分中也获得了最高分。
实验结果表明，当同时使用低剂量CT成像技术和有限角度CT成像技术时，所提出的方法能够有效地恢复CT图像。这项工作证明了有限角度CT成像技术可用于降低CT辐射剂量，也为低剂量CT成像研究提供了新思路。
