Dynamic Positron Emission Tomography Data-Driven Analysis Using Sparse Bayesian Learning

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on medical imaging 2008-09, Vol.27 (9), p.1356-1369
Main Authors: Jyh-Ying Peng, Aston, J.A.D., Gunn, R.N., Cheng-Yuan Liou, Ashburner, J.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Basis pursuit
Bayes Theorem
Bayesian methods
Biological system modeling
Blood
Brain - diagnostic imaging
compartmental models
Computer science
Data analysis
DEPICT
Gunn devices
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
nonnegative least squares
Parameter estimation
Pattern Recognition, Automated - methods
Plasma measurements
Positron emission tomography
Positron-Emission Tomography - methods
Power system modeling
Reproducibility of Results
Sensitivity and Specificity
time course analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2008.922185