Line Integral Alternating Minimization Algorithm for Dual-Energy X-Ray CT Image Reconstruction.
Line Integral Alternating Minimization Algorithm for Dual-Energy X-Ray CT Image Reconstruction.
复制标题
双能量X射线CT图像重建的线积分交流算法。
DOI：
10.1109/tmi.2015.2490658
复制
发表时间：
2016-02
影响因子：
10.6
通讯作者：
Williamson JF
中科院分区：
文献类型：
--
作者：
Chen Y;O'Sullivan JA;Politte DG;Evans JD;Han D;Whiting BR;Williamson JF
提出了一种新的双能X射线CT图像重建算法，称为线积分交替最小化（LIAM）。LIAM不是通过最小化数据和平均估计值之间的差异来获得分量图像，而是允许基础材料投影和基础正弦图之间的可调差异。引入了一个参数来控制这种差异的大小，并且使用该参数，新算法可以从两步方法连续地进行到联合估计方法。LIAM在迭代更新分量图像的线积分和使用图像迭代去模糊算法重建分量图像之间交替。在迭代去模糊步骤中可以结合边缘保持罚函数以降低分量图像中的粗糙度。通过LIAM重建来自临床扫描仪的模拟和实验采集的正弦图的图像，同时改变正则化参数以确定良好的选择。将应用于相同数据的双能量交替最小化算法的结果用于比较。使用一小部分的双能量交替最小化的计算时间，LIAM实现了更好的精度的组件图像的存在下，泊松噪声的模拟数据重建，并实现了相同水平的精度为真实的数据重建。
We propose a new algorithm, called line integral alternating minimization (LIAM), for dual-energy X-ray CT image reconstruction. Instead of obtaining component images by minimizing the discrepancy between the data and the mean estimates, LIAM allows for a tunable discrepancy between the basis material projections and the basis sinograms. A parameter is introduced that controls the size of this discrepancy, and with this parameter the new algorithm can continuously go from a two-step approach to the joint estimation approach. LIAM alternates between iteratively updating the line integrals of the component images and reconstruction of the component images using an image iterative deblurring algorithm. An edge-preserving penalty function can be incorporated in the iterative deblurring step to decrease the roughness in component images. Images from both simulated and experimentally acquired sinograms from a clinical scanner were reconstructed by LIAM while varying the regularization parameters to identify good choices. The results from the dual-energy alternating minimization algorithm applied to the same data were used for comparison. Using a small fraction of the computation time of dual-energy alternating minimization, LIAM achieves better accuracy of the component images in the presence of Poisson noise for simulated data reconstruction and achieves the same level of accuracy for real data reconstruction.