
HYBRID REGULARIZED CONEBEAM RECONSTRUCTION FOR AXIALLY SYMMETRIC OBJECT TOMOGRAPHY
李兴娥, 魏素花, 许海波, 陈冲
数学物理学报(英文版). 2022 (1):
403419.
DOI: 10.1007/s104730220122z
In this paper, we consider 3D tomographic reconstruction for axially symmetric objects from a single radiograph formed by conebeam Xrays. All contemporary density reconstruction methods in highenergy Xray radiography are based on the assumption that the cone beam can be treated as fan beams located at parallel planes perpendicular to the symmetric axis, so that the density of the whole object can be recovered layer by layer. Considering the relationship between different layers, we undertake the conebeam global reconstruction to solve the ambiguity effect at the material interfaces of the reconstruction results. In view of the anisotropy of classical discrete total variations, a new discretization of total variation which yields sharp edges and has better isotropy is introduced in our reconstruction model. Furthermore, considering that the object density consists of continually changing parts and jumps, a highorder regularization term is introduced. The final hybrid regularization model is solved using the alternating proximal gradient method, which was recently applied in image processing. Density reconstruction results are presented for simulated radiographs, which shows that the proposed method has led to an improvement in terms of the preservation of edge location.
参考文献 
相关文章 
计量指标
