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An iterative reconstruction method for sparse-projection data for low-dose CT
Journal of X-Ray Science and Technology ( IF 1.7 ) Pub Date : 2021-08-04 , DOI: 10.3233/xst-210906
Ying Huang 1, 2 , Qian Wan 2, 3 , Zixiang Chen 2 , Zhanli Hu 2 , Guanxun Cheng 4 , Yulong Qi 4
Affiliation  

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 usuallylead 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.

中文翻译:

一种低剂量CT稀疏投影数据的迭代重建方法

减少 X 射线辐射有利于降低患者患癌症的风险。实现这一目标有两种主要方法,一种是减少 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,产生过多的噪声和严重的边缘伪影,严重影响诊断结果。为了克服这种限制,本研究提出并测试了一种基于引导核过滤的算法。该算法结合相邻图像体素之间各向异性边缘的特点,用指数函数表示相关权重,并通过局部灰度梯度自适应地调整权重,以更好地保留图像结构,同时抑制噪声信息。实验表明,所提出的方法可以有效地抑制噪声并保留图像结构。与同类算法相比,该算法大大提高了重建图像的峰值信噪比(PSNR)、结构相似度(SSIM)和均方根误差(RMSE)。所提算法在定量分析中效果最好,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,这项研究表明,所提出的方法可以减少重复 CT 扫描所需的投影数量,并具有降低辐射剂量的医疗应用潜力。实验表明,所提出的方法可以有效地抑制噪声并保留图像结构。与同类算法相比,该算法大大提高了重建图像的峰值信噪比(PSNR)、结构相似度(SSIM)和均方根误差(RMSE)。所提算法在定量分析中效果最好,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,这项研究表明,所提出的方法可以减少重复 CT 扫描所需的投影数量,并具有降低辐射剂量的医疗应用潜力。实验表明,所提出的方法可以有效地抑制噪声并保留图像结构。与同类算法相比,该算法大大提高了重建图像的峰值信噪比(PSNR)、结构相似度(SSIM)和均方根误差(RMSE)。所提算法在定量分析中效果最好,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,这项研究表明,所提出的方法可以减少重复 CT 扫描所需的投影数量,并具有降低辐射剂量的医疗应用潜力。该算法大大提高了重建图像的峰值信噪比(PSNR)、结构相似度(SSIM)和均方根误差(RMSE)。所提算法在定量分析中效果最好,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,这项研究表明,所提出的方法可以减少重复 CT 扫描所需的投影数量,并具有降低辐射剂量的医疗应用潜力。该算法大大提高了重建图像的峰值信噪比(PSNR)、结构相似度(SSIM)和均方根误差(RMSE)。所提算法在定量分析中效果最好,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,这项研究表明,所提出的方法可以减少重复 CT 扫描所需的投影数量,并具有降低辐射剂量的医疗应用潜力。
更新日期:2021-08-07
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