Spatio-temporal prior based Kinetic Model in Dynamic PET reconstruction
Based on Markov Random Fields (MRF) theory, Bayesian approaches have been accepted as effective solutions to overcome the ill-posed problems of image restoration and reconstruction. Traditionally, the knowledge in most of prior models comes from simply weighted differences between the pixel intensities within a small local neighborhood, so it can only provide limited prior information for regularization. A novel dynamic image reconstruction method for PET is proposed which uses a spatio-temporal prior that constrains not only neighborhood information but also voxels behaviour in time to conform to 2-tissue compartmental model.
Markov Random Fields PET reconstruction two-tissue compartmental model
ZHANG QINGPING ZHANJIE
Shenzhen polytechnic School of Electronics and Information Engineering Shenzhen China Huangshi Food and Drug Administration Huangshi China
国际会议
上海
英文
495-499
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）