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Dynamic positron emission tomography data-driven analysis using sparse Bayesian learning
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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. doi:10.1109/TMI.2008.922185
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Official URL: http://dx.doi.org/10.1109/TMI.2008.922185
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 |
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| 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 | ||||
| Official Date: | September 2008 | ||||
| Dates: |
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| Volume: | Vol.27 | ||||
| Number: | No.9 | ||||
| Number of Pages: | 14 | ||||
| Page Range: | pp. 1356-1369 | ||||
| DOI: | 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| Status: | Not Peer Reviewed | ||||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) |
Data sourced from Thomson Reuters' Web of Knowledge
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