Fast iterative adaptive reconstruction in low-dose CT imaging

被引：12

作者：

Cheng, Lin ^{[1
]}

Chen, Yunqiang ^{[2
]}

Fang, Tong ^{[2
]}

Tyan, Jason ^{[2
]}

机构：

[1] Carnegie Mellon Univ, ECE Dept, Pittsburgh, PA 15213 USA

[2] Siemens Corp Res Inc, Intelligent Vis & Reasoning Dept, Princeton, NJ 08540 USA

来源：

2006 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 2006, PROCEEDINGS | 2006年

关键词：

image reconstruction; medical imaging; adaptive filters; medical diagnosis; iterative methods; tomography; optimization methods; radon transforms;

D O I：

10.1109/ICIP.2006.312511

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Traditional iterative tomographic reconstruction methods resort to gradient decent methods and require significant computation due to slow convergence. We divide the iterative reconstruction into two stages in the image and Radon spaces, respectively. First, we refine the reconstruction result by image space adaptive filtering. This finds a feasible update direction based on signal modeling. Second, we minimize the discrepancy between the sinograms along the update direction in Radon space and guarantee convergence. Reconstruction from clinical data using the proposed algorithm converges extremely fast and provides satisfactory reconstruction results in far fewer iterations than traditional methods.

引用

页码：889 / +

页数：2

共 8 条

[1]

Alvino CV, 2004, PROC CVPR IEEE, P576

[2]

Bar L, 2005, LECT NOTES COMPUT SC, V3459, P107

[3] A fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation [J].