Efficient Robust Reconstruction of Dynamic PET Activity Maps with Radioisotope Decay Constraints
Dynamic PET imaging performs sequence of data acquisition in order to provide visualization and quantification of physiological changes in specific tissues and organs. The reconstruction of activity maps is generally the first step in dynamic PET. State space H∞ approaches have been proved to be a robust method for PET image reconstruction where, however, temporal constraints are not considered during the reconstruction process. In addition, the state space strategies for PET image reconstruction have been computationally prohibitive for practical usage because of the need for matrix inversion. In this paper, we present a minimax formulation of the dynamic PET imaging problem where a radioisotope decay model is employed as physics-based temporal constraints on the photon counts. Furthermore, a robust steady state H∞ filter is developed to significantly improve the computational efficiency with minimal loss of accuracy. Experiments are conducted on Monte Carlo simulated image sequences for quantitative analysis and validation.
Fei Gao Huafeng Liu Pengcheng Shi
Golisano College of Computing and Information Sciences,Rochester Institute of Technology, Rochester, State Key Laboratory of Modern Optical Instrumentation, Zhejiang University,Hangzhou, 310027, China Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester
国际会议
北京
英文
571–578
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）