College of Science, Xi'an University of Technology, Xi'an, China.
The Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China.
J Xray Sci Technol. 2018;26(4):603-622. doi: 10.3233/XST-17358.
Excessive radiation exposure in computed tomography (CT) scans increases the chance of developing cancer and has become a major clinical concern. Recently, statistical iterative reconstruction (SIR) with l0-norm dictionary learning regularization has been developed to reconstruct CT images from the low dose and few-view dataset in order to reduce radiation dose. Nonetheless, the sparse regularization term adopted in this approach is l0-norm, which cannot guarantee the global convergence of the proposed algorithm. To address this problem, in this study we introduced the l1-norm dictionary learning penalty into SIR framework for low dose CT image reconstruction, and developed an alternating minimization algorithm to minimize the associated objective function, which transforms CT image reconstruction problem into a sparse coding subproblem and an image updating subproblem. During the image updating process, an efficient model function approach based on balancing principle is applied to choose the regularization parameters. The proposed alternating minimization algorithm was evaluated first using real projection data of a sheep lung CT perfusion and then using numerical simulation based on sheep lung CT image and chest image. Both visual assessment and quantitative comparison using terms of root mean square error (RMSE) and structural similarity (SSIM) index demonstrated that the new image reconstruction algorithm yielded similar performance with l0-norm dictionary learning penalty and outperformed the conventional filtered backprojection (FBP) and total variation (TV) minimization algorithms.
在计算机断层扫描（CT）扫描中过度的辐射暴露会增加患癌症的几率，这已成为临床关注的主要问题。最近，统计迭代重建（SIR）与 l0-范数字典学习正则化已被开发出来，以便从低剂量和少视角数据集重建 CT 图像，从而降低辐射剂量。然而，该方法采用的稀疏正则化项是 l0-范数，这不能保证所提出算法的全局收敛性。为了解决这个问题，在本研究中，我们将 l1-范数字典学习惩罚引入到 SIR 框架中，用于低剂量 CT 图像重建，并开发了一种交替最小化算法来最小化相关的目标函数，将 CT 图像重建问题转化为稀疏编码子问题和图像更新子问题。在图像更新过程中，应用了一种基于平衡原理的高效模型函数方法来选择正则化参数。所提出的交替最小化算法首先使用绵羊肺 CT 灌注的真实投影数据进行评估，然后使用基于绵羊肺 CT 图像和胸部图像的数值模拟进行评估。使用均方根误差（RMSE）和结构相似性（SSIM）指标的视觉评估和定量比较均表明，新的图像重建算法具有与 l0-范数字典学习惩罚相似的性能，优于传统的滤波反投影（FBP）和全变分（TV）最小化算法。
