期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 27, 期 9, 页码 1356-1369出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2008.922185
关键词
basis pursuit; compartmental models; DEPICT; nonnegative least squares; time course analysis
类别
资金
- National Science Council (Taiwan) [NSC-94-2118-M-001-014, NSC-95-2118-M-001-003]
A method is presented tor the analysis of dynamic positron emission tomography (PET) data using sparse Bayesian learning. Parameters are estimated in a compartmental framework using an over-complete exponential basis set and sparse Bayesian learning. The technique is applicable to analyses requiring either a plasma or reference tissue input function and produces estimates of the system's macro-parameters and model order. In addition, the Bayesian approach returns the posterior distribution which allows for some characterisation of the error component. The method is applied to the estimation of parametric images of neuroreceptor radioligand studies.
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