Peng, Jyh-Ying, Aston, John A. D., Gunn, Roger N., Liou, Cheng-Yuan and Ashburner, John. (2008) Dynamic positron emission tomography data-driven analysis using sparse Bayesian learning. IEEE Transactions on Medical Imaging, Vol.27 (No.9). pp. 1356-1369. ISSN 0278-0062

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Abstract

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.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics R Medicine > RC Internal medicine
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Tomography, Emission, Bayesian statistical decision theory, Compartmental analysis (Biology), Time-series analysis, Least squares
Journal or Publication Title:	IEEE Transactions on Medical Imaging
Publisher:	IEEE
ISSN:	0278-0062
Date:	September 2008
Volume:	Vol.27
Number:	No.9
Number of Pages:	14
Page Range:	pp. 1356-1369
Identification Number:	10.1109/TMI.2008.922185
Status:	Not Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Guo jia zi ran ke xue ji jin wei yuan hui (China) [National Natural Science Foundation of China] (NSFC)
Grant number:	NSC-94-2118-M-001-014 (NSFC), NSC-95-2118-M-001-003 (NSFC)
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URI:	http://wrap.warwick.ac.uk/id/eprint/29422

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