Zhao Zhenhua, Yang Jiansheng, Jiang Ming
LMAM, School of Mathematical Sciences, Peking University, Beijing, China.
Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing, China.
J Xray Sci Technol. 2015;23(3):349-64. doi: 10.3233/XST-150494.
Interior tomography as a promising X-ray imaging technique has received increasing attention in medical imaging field. In our previous works, we proposed a high-order total variation (HOT) minimization method for interior tomography and proved that the region of interest (ROI) can be reconstructed accurately by minimizing the HOT if the object image is piecewise polynomial within the ROI. In this paper, we propose a modified HOT (MHOT) and develop a fast MHOT minimization algorithm for interior tomography, based on split Bregman iteration and ordered-subset simultaneous algebraic reconstruction techniques (OS-SART). Numerical simulation demonstrates that our algorithm is computationally efficient and can be applied to obtain high-quality reconstructed image.
内部断层扫描作为一种很有前景的X射线成像技术，在医学成像领域受到了越来越多的关注。在我们之前的工作中，我们提出了一种用于内部断层扫描的高阶全变差（HOT）最小化方法，并证明如果目标图像在感兴趣区域（ROI）内是分段多项式的，那么通过最小化HOT可以准确重建ROI。在本文中，我们提出了一种改进的HOT（MHOT），并基于分裂Bregman迭代和有序子集同时代数重建技术（OS-SART）开发了一种用于内部断层扫描的快速MHOT最小化算法。数值模拟表明，我们的算法计算效率高，可用于获得高质量的重建图像。
