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基于多方向梯度算子的稀疏视角 CT 重建性能。

Performance of sparse-view CT reconstruction with multi-directional gradient operators.

机构信息

Department of Biomedical Engineering, National Yang-Ming University, Taipei, Taiwan, R.O.C.

Department of Biomedical Imaging & Radiological Sciences, National Yang-Ming University, Taipei, Taiwan, R.O.C.

出版信息

PLoS One. 2019 Jan 7;14(1):e0209674. doi: 10.1371/journal.pone.0209674. eCollection 2019.

DOI:10.1371/journal.pone.0209674
PMID:30615635
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6322781/
Abstract

To further reduce the noise and artifacts in the reconstructed image of sparse-view CT, we have modified the traditional total variation (TV) methods, which only calculate the gradient variations in x and y directions, and have proposed 8- and 26-directional (the multi-directional) gradient operators for TV calculation to improve the quality of reconstructed images. Different from traditional TV methods, the proposed 8- and 26-directional gradient operators additionally consider the diagonal directions in TV calculation. The proposed method preserves more information from original tomographic data in the step of gradient transform to obtain better reconstruction image qualities. Our algorithms were tested using two-dimensional Shepp-Logan phantom and three-dimensional clinical CT images. Results were evaluated using the root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and universal quality index (UQI). All the experiment results show that the sparse-view CT images reconstructed using the proposed 8- and 26-directional gradient operators are superior to those reconstructed by traditional TV methods. Qualitative and quantitative analyses indicate that the more number of directions that the gradient operator has, the better images can be reconstructed. The 8- and 26-directional gradient operators we proposed have better capability to reduce noise and artifacts than traditional TV methods, and they are applicable to be applied to and combined with existing CT reconstruction algorithms derived from CS theory to produce better image quality in sparse-view reconstruction.

摘要

为了进一步降低稀疏视角 CT 重建图像中的噪声和伪影，我们对传统的全变差（TV）方法进行了修改，传统的 TV 方法仅计算 x 和 y 方向的梯度变化，我们提出了 8 向和 26 向（多向）梯度算子用于 TV 计算，以提高重建图像的质量。与传统的 TV 方法不同，所提出的 8 向和 26 向梯度算子在 TV 计算中另外考虑了对角方向。该方法在梯度变换步骤中保留了更多来自原始断层扫描数据的信息，从而获得更好的重建图像质量。我们的算法使用二维 Shepp-Logan 体模和三维临床 CT 图像进行了测试。使用均方根误差（RMSE）、峰值信噪比（PSNR）和通用质量指数（UQI）对结果进行了评估。所有实验结果均表明，使用所提出的 8 向和 26 向梯度算子重建的稀疏视角 CT 图像优于传统 TV 方法重建的图像。定性和定量分析表明，梯度算子具有的方向越多，重建的图像越好。与传统的 TV 方法相比，我们提出的 8 向和 26 向梯度算子具有更好的降噪和去伪影能力，它们适用于与现有的基于 CS 理论的 CT 重建算法结合使用，以在稀疏视角重建中产生更好的图像质量。

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