Niu Shanzhou, Zhang Mengzhen, Qiu Yang, Li Shuo, Liang Lijing, Liu Qiegen, Niu Tianye, Wang Jing, Ma Jianhua
School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, People's Republic of China.
Ganzhou Key Laboratory of Computational Imaging , Gannan Normal University, Ganzhou 341000, People's Republic of China.
Phys Med Biol. 2024 Apr 26;69(10). doi: 10.1088/1361-6560/ad3c0b.
The x-ray radiation dose in computed tomography (CT) examination has been a major concern for patients. Lowing the tube current and exposure time in data acquisition is a straightforward and cost-effective strategy to reduce the x-ray radiation dose. However, this will inevitably increase the noise fluctuations in measured projection data, and the corresponding CT image quality will be severely degraded if noise suppression is not performed during image reconstruction. To reconstruct high-quality low-dose CT image, we present a spatial-radon domain total generalized variation (SRDTGV) regularization for statistical iterative reconstruction based on penalized weighted least-squares (PWLS) principle, which is called PWLS-SRDTGV for simplicity. The presented PWLS-SRDTGV model can simultaneously reconstruct high-quality CT image in space domain and its corresponding projection in radon domain. An efficient split Bregman algorithm was applied to minimize the cost function of the proposed reconstruction model. Qualitative and quantitative studies were performed to evaluate the effectiveness of the PWLS-SRDTGV image reconstruction algorithm using a digital 3D XCAT phantom and an anthropomorphic torso phantom. The experimental results demonstrate that PWLS-SRDTGV algorithm achieves notable gains in noise reduction, streak artifact suppression, and edge preservation compared with competing reconstruction approaches.
计算机断层扫描（CT）检查中的X射线辐射剂量一直是患者主要关注的问题。在数据采集中降低管电流和曝光时间是降低X射线辐射剂量的一种直接且经济高效的策略。然而，这将不可避免地增加测量投影数据中的噪声波动，如果在图像重建过程中不进行噪声抑制，相应的CT图像质量将严重下降。为了重建高质量的低剂量CT图像，我们基于惩罚加权最小二乘（PWLS）原理，提出了一种用于统计迭代重建的空间拉东域全广义变分（SRDTGV）正则化方法，简称为PWLS-SRDTGV。所提出的PWLS-SRDTGV模型可以同时在空间域重建高质量的CT图像及其在拉东域的相应投影。应用了一种高效的分裂Bregman算法来最小化所提出的重建模型的代价函数。使用数字3D XCAT体模和拟人化躯干体模进行了定性和定量研究，以评估PWLS-SRDTGV图像重建算法的有效性。实验结果表明，与竞争的重建方法相比，PWLS-SRDTGV算法在降噪、条纹伪影抑制和边缘保留方面取得了显著进展。
