Huang Ying, Wan Qian, Chen Zixiang, Hu Zhanli, Cheng Guanxun, Qi Yulong
School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China.
Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
J Xray Sci Technol. 2021;29(5):797-812. doi: 10.3233/XST-210906.
Reducing X-ray radiation is beneficial for reducing the risk of cancer in patients. There are two main approaches for achieving this goal namely, one is to reduce the X-ray current, and another is to apply sparse-view protocols to do image scanning and projections. However, these techniques usually lead to degradation of the reconstructed image quality, resulting in excessive noise and severe edge artifacts, which seriously affect the diagnosis result. In order to overcome such limitation, this study proposes and tests an algorithm based on guided kernel filtering. The algorithm combines the characteristics of anisotropic edges between adjacent image voxels, expresses the relevant weights with an exponential function, and adjusts the weights adaptively through local gray gradients to better preserve the image structure while suppressing noise information. Experiments show that the proposed method can effectively suppress noise and preserve the image structure. Comparing with similar algorithms, the proposed algorithm greatly improves the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) of the reconstructed image. The proposed algorithm has the best effect in quantitative analysis, which verifies the effectiveness of the proposed method and good image reconstruction performance. Overall, this study demonstrates that the proposed method can reduce the number of projections required for repeated CT scans and has potential for medical applications in reducing radiation doses.
减少X射线辐射有利于降低患者患癌风险。实现这一目标主要有两种方法，一种是降低X射线电流，另一种是应用稀疏视图协议进行图像扫描和投影。然而，这些技术通常会导致重建图像质量下降，产生过多噪声和严重的边缘伪影，严重影响诊断结果。为了克服这种局限性，本研究提出并测试了一种基于引导核滤波的算法。该算法结合了相邻图像体素之间各向异性边缘的特征，用指数函数表示相关权重，并通过局部灰度梯度自适应调整权重，以在抑制噪声信息的同时更好地保留图像结构。实验表明，该方法能有效抑制噪声并保留图像结构。与类似算法相比，该算法大大提高了重建图像的峰值信噪比（PSNR）、结构相似性（SSIM）和均方根误差（RMSE）。该算法在定量分析中效果最佳，验证了所提方法的有效性和良好的图像重建性能。总体而言，本研究表明所提方法可减少重复CT扫描所需的投影数量，并在降低辐射剂量的医学应用中具有潜力。
