Objective: We propose a sparse-view helical CT iterative reconstruction algorithm based on projection of convex set tensor total generalized variation minimization (TTGV-POCS) to reduce the X-ray dose of helical CT scanning.
Methods: The three-dimensional volume data of helical CT reconstruction was viewed as the third-order tensor. The tensor generalized total variation (TTGV) was used to describe the structural sparsity of the three-dimensional image. The POCS iterative reconstruction framework was adopted to achieve a robust result of sparse-view helical CT reconstruction. The TTGV-POCS algorithm fully used the structural sparsity of first-order and second-order derivation and the correlation between the slices of helical CT image data to effectively suppress artifacts and noise in the image of sparse-view reconstruction and better preserve image edge information.
Results: The experimental results of XCAT phantom and patient scan data showed that the TTGVPOCS algorithm had better performance in reducing noise, removing artifacts and maintaining edges than the existing reconstruction algorithms. Comparison of the sparse-view reconstruction results of XCAT phantom data with 144 exposure views showed that the TTGV-POCS algorithm proposed herein increased the PSNR quantitative index by 9.17%-15.24% compared with the experimental comparison algorithm; the FSIM quantitative index was increased by 1.27%-9.30%.
Conclusions: The TTGV-POCS algorithm can effectively improve the image quality of helical CT sparse-view reconstruction and reduce the radiation dose of helical CT examination to improve the clinical imaging diagnosis.
目的: 为减少螺旋CT扫描X射线辐射剂量，提出一种基于凸集投影的张量广义全变分最小（TTGV-POCS）的稀疏角度螺旋CT迭代重建算法。
方法: 将螺旋CT三维体数据看作三阶张量，利用张量广义全变分（TTGV）最小约束刻画其三维图像的数据特性，并纳入凸集投影迭代重建框架，实现稀疏角度螺旋CT的鲁棒重建。TTGV-POCS算法充分利用螺旋CT图像数据的一阶梯度与二阶梯度的空间结构稀疏性和三维数据层间相关性，可有效抑制稀疏角度重建图像中的伪影与噪声，并较好保持图像边缘信息。
结果: XCAT体模数据与病人扫描数据的实验结果表明，TTGV-POCS算法相比现有重建算法在降低噪声、去除伪影和保持边缘等方面均有较好的表现；比较XCAT体模数据稀疏角度重建结果，本文提出的TTGV-POCS算法相比现有重建算法PSNR定量指标可提升9.17%~15.24%；FSIM定量指标可提升1.27%~9.30%。
结论: TTGV-POCS算法可有效改善稀疏角度螺旋CT重建图像质量，降低螺旋CT检查辐射剂量，更好服务于临床影像诊断。
Keywords: helical CT; projection on convex set; sparse-view; tensor total generalized variation.