OBJECTIVE:In order to solve the blurred structural details and over-smoothing effects in sparse representation dictionary learning reconstruction algorithm, this study aims to test sparse angle CT reconstruction with weighted dictionary learning algorithm based on adaptive Group-Sparsity Regularization (AGSR-SART). METHODS:First, a new similarity measure is defined in which Covariance is introduced into Euclidean distance, Non-local image patches are adaptively divided into groups of different sizes as the basic unit of sparse representation. Second, the weight factor of the regular constraint terms is designed through the residuals represented by the dictionary, so that the algorithm takes different smoothing effects on different regions of the image during the iterative process. The sparse reconstructed image is modified according to the difference between the estimated value and the intermediate image. Last, The SBI (Split Bregman Iteration) iterative algorithm is used to solve the objective function. An abdominal image, a pelvic image and a thoracic image are employed to evaluate performance of the proposed method. RESULTS:In terms of quantitative evaluations, experimental results show that new algorithm yields PSNR of 48.20, the maximum SSIM of 99.06% and the minimum MAE of 0.0028. CONCLUSIONS:This study demonstrates that new algorithm can better preserve structural details in reconstructed CT images. It eliminates the effect of excessive smoothing in sparse angle reconstruction, enhances the sparseness and non-local self-similarity of the image, and thus it is superior to several existing reconstruction algorithms.

中文翻译：

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基于自适应群稀疏正则化的加权字典学习算法稀疏角CT重建

目的：为了解决稀疏表示字典学习重建算法中结构细节模糊和过度平滑的问题，本研究旨在测试基于自适应组稀疏正则化（AGSR-SART）的加权字典学习算法的稀疏角CT重建。方法：首先定义了一种新的相似性度量，在欧几里德距离中引入协方差，将非局部图像块自适应地分成不同大小的组作为稀疏表示的基本单位。其次，通过字典表示的残差设计正则约束项的权重因子，使算法在迭代过程中对图像的不同区域采取不同的平滑效果。根据估计值与中间图像的差值对稀疏重构图像进行修正。最后，采用SBI（Split Bregman Iteration）迭代算法求解目标函数。采用腹部图像、骨盆图像和胸部图像来评估所提出方法的性能。结果：在定量评估方面，实验结果表明，新算法的 PSNR 为 48.20，最大 SSIM 为 99.06%，最小 MAE 为 0.0028。结论：本研究表明，新算法可以更好地保留重建 CT 图像中的结构细节。它消除了稀疏角重建中过度平滑的影响，增强了图像的稀疏性和非局部自相似性，优于现有的几种重建算法。