Affiliations: [a] School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China
| [b] Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| [c] Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, China
| [d] Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
Correspondence:
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Corresponding author: Yulong Qi, Department of Radiology, Peking University Shenzhen Hospital, Shenzhen 518036, China. E-mail: [email protected].
Note: [1] Ying Huang and Qian Wan are co-first authors.
Abstract: 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.
Keywords: X-ray computed tomography (CT), image reconstruction, total variation (TV), reduction of X-ray dose, reduction of image noise
