Bayesian Inference Based Reconstruction for Poisson Statistics

被引：0

作者：

Dey, Joyoni ^{[1
]}

Xu, Jingzhu ^{[1
]}

Bhusal, Narayan ^{[1
]}

Shumilov, Dmytro ^{[1
]}

机构：

[1] Louisiana State Univ, Dept Phys & Astron, Baton Rouge, LA 70803 USA

来源：

2015 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC) | 2015年

关键词：

D O I：

暂无

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

A new reconstruction method is explored using Bayesian inference for Poisson Statistics for emission tomography. The Gamma density function is chosen as the natural choice for the activity distribution at each voxel, being the conjugate-prior of Poisson distribution. The update equations of the shape and rate parameters for Gamma distribution are derived and tested on a simple 2D example using Metropolis Hastings algorithm. The results show promise with quick convergence within similar to 20 iterations and stable noise properties with iteration. A 3D algorithm and comparison with OSEM is underway.

引用

页数：3

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