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	Study on GDMRF model used in high energy transmission ICT
	Kejun Kang ; Ziran Zhao ; Zhiqiang Chen ; Li Zhang
	2010-05-06 ; 2010-05-06
关键词	Practical Theoretical or Mathematical Experimental/ convolution emission tomography image reconstruction image resolution iterative methods Markov processes maximum likelihood estimation random processes/ genetic discrete Markov random filed model high energy transmission ICT industry computed tomography convolution backprojection method statistical methods emission tomography Poisson noise X-ray photons maximum a posteriori estimation iterate algorithm prior image model discrete gray set genetic character image quality reconstruction SNR signal-to-noise ratio/ A8760K Nuclear medicine, emission tomography A0250 Probability theory, stochastic processes, and statistics A0260 Numerical approximation and analysis A8710 General, theoretical, and mathematical biophysics B7510R Nuclear medicine, emission tomography B6135 Optical, image and video signal processing B0240J Markov processes B0290F Interpolation and function approximation (numerical analysis) C5260B Computer vision and image processing techniques C1140J Markov processes C4130 Interpolation and function approximation (numerical analysis)
中文摘要	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 Poisson 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).
语种	英语 ; 英语
出版者	IOS Press ; Netherlands
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/11514]
专题	清华大学
推荐引用方式 GB/T 7714	Kejun Kang,Ziran Zhao,Zhiqiang Chen,et al. Study on GDMRF model used in high energy transmission ICT[J],2010, 2010.
APA	Kejun Kang,Ziran Zhao,Zhiqiang Chen,&Li Zhang.(2010).Study on GDMRF model used in high energy transmission ICT..
MLA	Kejun Kang,et al."Study on GDMRF model used in high energy transmission ICT".(2010).

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