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Maximum-Likelihood Dual-Energy TomographicImage Reconstruction

By Jeffrey A. Fessler, Idris Elbakri, Predrag Sukovic and Neal H. Clinthorne

Abstract

Dual-energy (DE) X-ray computed tomography (CT) has shown promise for material characterization and for providing quantitatively accurate CT values in a variety of applications. However, DE-CT has not been used routinely in medicine to date, primarily due to dose considerations. Most methods for DE-CT have used the filtered backprojection method for image reconstruction, leading to suboptimal noise/dose properties. This paper describes a statistical (maximum-likelihood) method for dual-energy X-ray CT that accommodates a wide variety of potential system configurations and measurement noise models. Regularized methods (such as penalized-likelihood or Bayesian estimation) are straightforward extensions. One version of the algorithm monotonically decreases the negative log-likelihood cost function each iteration. An ordered-subsets variation of the algorithm provides a fast and practical version

Publisher: SPIE
Year: 2002
DOI identifier: 10.1117/12.467189
OAI identifier: oai:deepblue.lib.umich.edu:2027.42/85934
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