Information Engineering University, Zhengzhou, Henan, China.
J Xray Sci Technol. 2022;30(3):613-630. doi: 10.3233/XST-211095.
Image reconstruction for realistic medical images under incomplete observation is still one of the core tasks for computed tomography (CT). However, the stair-case artifacts of Total variation (TV) based ones have restricted the usage of the reconstructed images.
This work aims to propose and test an accurate and efficient algorithm to improve reconstruction quality under the idea of synergy between local and nonlocal regularizations.
The total variation combining the nonlocal means filtration is proposed and the alternating direction method of multipliers is utilized to develop an efficient algorithm. The first order approximation of linear expansion at intermediate point is applied to overcome the computation of the huge CT system matrix.
The proposed method improves root mean squared error by 25.6% compared to the recent block-matching sparsity regularization (BMSR) on simulation dataset of 19 views. The structure similarities of image of the new method is higher than 0.95, while that of BMSR is about 0.92. Moreover, on real rabbit dataset of 20 views, the peak signal-to-noise ratio (PSNR) of the new method is 36.84, while using other methods PSNR are lower than 35.81.
The proposed method shows advantages on noise suppression and detail preservations over the competing algorithms used in CT image reconstruction.
在不完全观测下为真实医学图像进行图像重建仍然是计算机断层扫描（CT）的核心任务之一。然而，基于全变分（TV）的阶梯状伪影限制了重建图像的使用。
本工作旨在提出并测试一种基于局部和非局部正则化协同作用的准确有效的算法，以提高重建质量。
提出了一种将非局部均值滤波与全变分相结合的方法，并利用交替方向乘子法开发了一种有效的算法。在中间点应用线性展开的一阶近似来克服巨大的 CT 系统矩阵的计算。
与最近的 19 视图模拟数据集的块匹配稀疏正则化（BMSR）相比，所提出的方法将均方根误差提高了 25.6%。新方法的图像结构相似度高于 0.95，而 BMSR 的图像结构相似度约为 0.92。此外，在 20 视图的真实兔数据集上，新方法的峰值信噪比（PSNR）为 36.84，而使用其他方法的 PSNR 低于 35.81。
与 CT 图像重建中使用的竞争算法相比，所提出的方法在噪声抑制和细节保留方面显示出优势。
