Fu Jing, Feng Fei, Quan Huimin, Wan Qian, Chen Zixiang, Liu Xin, Zheng Hairong, Liang Dong, Cheng Guanxun, Hu Zhanli
Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
College of Electrical and Information Engineering, Hunan University, Changsha, China.
Quant Imaging Med Surg. 2021 Jun;11(6):2541-2559. doi: 10.21037/qims-20-963.
Radiation exposure computed tomography (CT) scans and the associated risk of cancer in patients have been major clinical concerns. Existing research can achieve low-dose CT imaging by reducing the X-ray current and the number of projections per rotation of the human body. However, this method may produce excessive noise and fringe artifacts in the traditional filtered back projection (FBP)-reconstructed image.
To solve this problem, iterative image reconstruction is a promising option to obtain high-quality images from low-dose scans. This paper proposes a patch-based regularization method based on penalized weighted least squares total variation (PWLS-PR) for iterative image reconstruction. This method uses neighborhood patches instead of single pixels to calculate the nonquadratic penalty. The proposed regularization method is more robust than the conventional regularization method in identifying random fluctuations caused by sharp edges and noise. Each iteration of the proposed algorithm can be described in the following three steps: image updating via the total variation based on penalized weighted least squares (PWLS-TV), image smoothing, and pixel-by-pixel image fusion.
Simulation and real-world projection experiments show that the proposed PWLS-PR algorithm achieves a higher image reconstruction performance than similar algorithms. Through the qualitative and quantitative evaluation of simulation experiments, the effectiveness of the method is also verified.
Furthermore, this study shows that the PWLS-PR method reduces the amount of projection data required for repeated CT scans and has the useful potential to reduce the radiation dose in clinical medical applications.
计算机断层扫描（CT）中的辐射暴露以及患者患癌的相关风险一直是主要的临床关注点。现有研究可以通过降低X射线电流和人体每旋转一周的投影数量来实现低剂量CT成像。然而，这种方法可能会在传统的滤波反投影（FBP）重建图像中产生过多噪声和条纹伪影。
为了解决这个问题，迭代图像重建是从低剂量扫描中获取高质量图像的一个有前景的选择。本文提出一种基于惩罚加权最小二乘总变差（PWLS-PR）的基于块的正则化方法用于迭代图像重建。该方法使用邻域块而不是单个像素来计算非二次惩罚项。所提出的正则化方法在识别由尖锐边缘和噪声引起的随机波动方面比传统正则化方法更稳健。所提算法的每次迭代可以通过以下三个步骤来描述：基于惩罚加权最小二乘总变差（PWLS-TV）的总变差进行图像更新、图像平滑以及逐像素图像融合。
模拟和实际投影实验表明，所提出的PWLS-PR算法比类似算法具有更高的图像重建性能。通过模拟实验的定性和定量评估，该方法的有效性也得到了验证。
此外，本研究表明，PWLS-PR方法减少了重复CT扫描所需的投影数据量，并且在临床医疗应用中具有降低辐射剂量的有用潜力。
