Abstract Low-dose computed tomography (LdCT) imaging can greatly reduce the radiation dose imposed to patient, however it leads to the low signal-to-noise ratio (SNR) measured projection data. Using conventional analytical reconstruction method (e.g., filtered back-projection method), the reconstruction results usually suffer from serious noise in LdCT. To obtain high-quality CT images, iterative reconstruction method combined with prior knowledge of the object is of great importance. In this work, both structural group sparsity and gradient prior sparsity are jointed as a novel regularization constraint in the proposed CT reconstruction model. To solve the optimization-based CT reconstruction problem, original problem was transformed into a series of sub-problems based on alternating direction method of multipliers framework. The merit of the proposed joint regularization method is that global and local sparsity are both utilized. To valid the performance of proposed reconstruction algorithm, we did simulated experiments with different noise levels and real data studies. The qualitative and quantitative analyses show that the proposed reconstruction algorithm has better performance than other iterative reconstruction algorithms. What's more, compared to the existing iterative reconstruction methods, the proposed reconstruction algorithm can well reconstructed important structure features and effectively suppress the noise and artifacts.

中文翻译：

---

低剂量计算机断层扫描重建由结构组稀疏性与梯度先验相结合

摘要 低剂量计算机断层扫描（LdCT）成像可以大大降低对患者施加的辐射剂量，但会导致测量投影数据的信噪比（SNR）低。使用传统的解析重建方法（例如滤波反投影方法），重建结果在LdCT中通常会受到严重的噪声影响。为了获得高质量的CT图像，结合物体先验知识的迭代重建方法非常重要。在这项工作中，结构组稀疏性和梯度先验稀疏性在所提出的 CT 重建模型中作为一种新的正则化约束联合起来。为解决基于优化的CT重建问题，基于乘法器框架的交替方向法将原问题转化为一系列子问题。所提出的联合正则化方法的优点是同时利用了全局和局部稀疏性。为了验证所提出的重建算法的性能，我们使用不同的噪声水平和真实数据研究进行了模拟实验。定性和定量分析表明，所提出的重建算法比其他迭代重建算法具有更好的性能。更重要的是，与现有的迭代重建方法相比，所提出的重建算法能够很好地重建重要的结构特征，有效地抑制噪声和伪影。定性和定量分析表明，所提出的重建算法比其他迭代重建算法具有更好的性能。更重要的是，与现有的迭代重建方法相比，所提出的重建算法能够很好地重建重要的结构特征，有效地抑制噪声和伪影。定性和定量分析表明，所提出的重建算法比其他迭代重建算法具有更好的性能。更重要的是，与现有的迭代重建方法相比，所提出的重建算法能够很好地重建重要的结构特征，有效地抑制噪声和伪影。