Paper Info
Title
Steady-state model of the radio-pharmaceutical uptake for MR-PET.
Abstract
This work explores a fully-automated algorithm for estimation of the uptake of radio-pharmaceutical in brain MR-PET imaging. The algorithm is based on a model of the pharmaceutical uptake coupled with probabilistic models of the PET and MR acquisition systems. In contrast to algorithms that attempt to correct for the partial volume effect (PVE), the problem is tackled here in the reconstruction by means of a probabilistic model of the pharmaceutical uptake. We make use of hybrid Bayesian networks to describe the joint probabilistic model and to obtain an efficient optimisation algorithm. We describe solutions adopted in order to mitigate the effect of local maxima and to reduce the sensitivity to the initialisation of the parameters, rendering the algorithm fully automatic. The algorithm is evaluated on simulated MR-PET data and on the reconstruction of clinical PET FDG acquisitions.
Year
DOI
Venue
2012
10.1007/978-3-642-33415-3_36
MICCAI
Keywords
Field
DocType
joint probabilistic model,efficient optimisation algorithm,mr acquisition system,hybrid bayesian networks,steady-state model,radio-pharmaceutical uptake,pharmaceutical uptake,brain mr-pet imaging,fully-automated algorithm,simulated mr-pet data,clinical pet fdg acquisition,probabilistic model,algorithms,radiopharmaceuticals,magnetic resonance imaging,poisson distribution,computer simulation,bayes theorem,probability,brain mapping
Computer vision,Steady State theory,Pattern recognition,Computer science,Maxima and minima,Bayesian network,Artificial intelligence,Statistical model,Probabilistic logic,Rendering (computer graphics),Partial volume
Conference
Volume
Issue
ISSN
15
Pt 1
0302-9743
Citations 
PageRank 
References 
1
0.36
4
Authors
5
Name
Order
Citations
PageRank
Stefano Pedemonte1636.80
Cardoso M. Jorge26413.70
Simon Arridge3474.64
Brian F. Hutton49814.33
Sébastien Ourselin557657.16