Dynamic positron emission tomography data-driven analysis using sparse Bayesian learning

Jyh-Ying Peng; John A D Aston; Roger N Gunn; Cheng-Yuan Liou; John Ashburner

doi:10.1109/TMI.2008.922185

Dynamic positron emission tomography data-driven analysis using sparse Bayesian learning

IEEE Trans Med Imaging. 2008 Sep;27(9):1356-69. doi: 10.1109/TMI.2008.922185.

Authors

Jyh-Ying Peng¹, John A D Aston, Roger N Gunn, Cheng-Yuan Liou, John Ashburner

Affiliation

¹ Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan.

PMID: 18753048
DOI: 10.1109/TMI.2008.922185

Abstract

A method is presented for 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.

Publication types

Evaluation Study
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Artificial Intelligence*
Bayes Theorem
Brain / diagnostic imaging*
Humans
Image Enhancement / methods*
Image Interpretation, Computer-Assisted / methods*
Imaging, Three-Dimensional / methods*
Pattern Recognition, Automated / methods*
Positron-Emission Tomography / methods*
Reproducibility of Results
Sensitivity and Specificity