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A variational bayesian inference method for parametric imaging of PET data

Abstract:

In dynamic Positron Emission Tomography (PET) studies, compartmental models provide the richest information on the tracer kinetics of the tissue. Inverting such models at the voxel level is however quite challenging due to the low signal-to-noise ratio of the time activity curves. In this study, we propose the use of a Variational Bayesian (VB) approach to efficiently solve this issue and thus obtain robust quantitative parametric maps.
VB was adapted to the non-uniform noise distributi...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.neuroimage.2017.02.009

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Grant:
MethodstoIntegrateBrainPhenotype
“NeuroimagingGenetics:Models
Genotype”
Publisher:
Elsevier Publisher's website
Journal:
NeuroImage Journal website
Volume:
150
Pages:
136-149
Publication date:
2017-02-14
Acceptance date:
2017-02-04
DOI:
ISSN:
1053-8119 and 1095-9572
Source identifiers:
675920
Keywords:
Pubs id:
pubs:675920
UUID:
uuid:4b441051-4ced-4bb0-8ca7-73ce5a6b7678
Local pid:
pubs:675920
Deposit date:
2017-02-06

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