In clinical cerebral perfusion CT examination, repeated scanning the region of interest in the cine mode increases the radiation dose of the patients, while decreasing the radiation dose by lowering the scanning current results in poor image quality and affects the clinical diagnosis. We propose a penalized weighted least-square (PWLS) method for recovering the projection data to improve the quality of low-dose cerebral perfusion CT imaged. This method incorporates the statistical distribution characteristics of brain perfusion CT projection data and uses the statistical properties of the projection data for modeling. The PWLS method was used to recover the data, and the Gauss-Seidel (GS) method was employed for iterative solving. Adaptive weighting is introduced between the original projection data and the projection data after PWLS restoration. The experimental results on the clinical data demonstrated that the PWLS-based sinogram restoration method improved noise reduction and artifact suppression as compared with the conventional noise reduction methods, and better retained the edges and details to generate better cerebral perfusion maps.
目的: 在临床脑灌注CT检查中，对感兴趣区域进行连续动态扫描会使患者承受较大的射线剂量。为降低扫描辐射剂量，对脑灌注CT检查常采用降低扫描电流的形式，但降低射线剂量会使得图像质量严重受损，影响临床诊断。针对这种情况，本文采用一种基于惩罚加权最小二乘 (PWLS) 投影数据恢复的方法用于低剂量脑灌注CT的优质成像。
方法: 该方法充分考虑脑灌注CT投影数据的统计分布特性，根据投影数据的统计特性进行建模，采用PWLS的方法进行数据恢复，然后利用高斯-赛德尔优化算法进行迭代求解。同时，该方法在原始投影数据和PWLS恢复后的投影数据之间引入自适应加权处理，可以更好地恢复投影数据。
结果: 临床数据实验结果表明，基于惩罚加权最小二乘的投影数据恢复方法相比常规的处理方法，能够更好地去除脑灌注CT图像噪声和伪影，同时较好保持图像边缘和细节，提高脑灌注参数图成像质量。
结论: 基于惩罚加权最小二乘的投影数据恢复方法能够更好地抑制脑灌注CT图像的噪声伪影，实现低剂量脑灌注CT优质成像。