title: A fast algorithm for high order total variation minimization based interior tomography.
abstract: 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.
authors.affiliation: LMAM, School of Mathematical Sciences, Peking University, Beijing, China.
authors.forename: Zhenhua
authors.initials: Z
authors.lastname: Zhao
authors.affiliation: LMAM, School of Mathematical Sciences, Peking University, Beijing, China.|Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing, China.
authors.forename: Jiansheng
authors.initials: J
authors.lastname: Yang
authors.affiliation: LMAM, School of Mathematical Sciences, Peking University, Beijing, China.|Beijing International Center for Mathematical Research, Peking University, Beijing, China.|Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D019047:Phantoms, Imaging; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Interior tomography; MHOT; OS-SART; l_1-regularized problem; split bregman iteration