Kejun Kang, Ziran Zhao, Zhiqiang Chen
2004
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Influential Citations
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Journal
Journal of X-ray Science and Technology
Abstract
In high-energy transmission industry computed tomography (ICT), convolution back-projection (CBP) is widely used for image reconstruction. But high mass thickness and the requirement of short scanning time lead to high statistical noise. Statistical methods are developed from emission tomography and its potential in transmission tomography has been appreciated recently. Consequently, statistical methods, which model the Possion noise of X-ray photons, are expected to produce better images than transform methods. In order to use those methods, such as maximum a posteriori (MAP), we need speed up the iterate algorithm and make use of the work-piece's prior information thoroughly. This paper presents a novel prior image model, which we refer to as genetic discrete Markov random filed (GDMRF) model. This model has two useful characters· discrete gray set and the genetic character of gray set. With the discrete gray set, the iterate algorithm is highly active. With the genetic character of GDMRF model, the reconstructed image quality benefits from using prior information thoroughly. Experiment has demonstrated that the GDMRF model is useful for image reconstruction from sinogram with low signal-to-noise ratio (SNR).