Statistical image reconstruction for polyenergetic X-ray computed tomography

被引：553

作者：

Elbakri, IA ^{[1
]}

Fessler, JA ^{[1
]}

机构：

[1] Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA

来源：

IEEE TRANSACTIONS ON MEDICAL IMAGING | 2002年 / 21卷 / 02期

关键词：

bearn hardening; penalized likelihood; statistical reconstruction; X-ray CT;

D O I：

10.1109/42.993128

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper describes a statistical image reconstruction method for X-ray computed tomography (CT) that is based on a physical model that accounts for the polyenergetic X-ray source spectrum and the measurement nonlinearities caused by energy-dependent attenuation. We assume that the object consists of a given number of nonoverlapping materials, such as soft tissue and bone. The attenuation coefficient of each voxel is the product of its unknown density and a known energy-dependent mass attenuation coefficient. We formulate a penalized-likelihood function for this polyenergetic model and develop an ordered-subsets iterative algorithm for estimating the unknown densities in each voxel. The algorithm monotonically decreases the cost function at each iteration when one subset Is used. Applying this method to simulated X-ray CT measurements of objects containing both bone and soft tissue yields images with significantly reduced beam hardening artifacts.

引用

页码：89 / 99

页数：11

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