Paper Info
Title
DIRECT RECONSTRUCTION OF DYNAMIC PET PARAMETRIC IMAGES USING SPARSE SPECTRAL REPRESENTATION.
Abstract
To generate parametric images for dynamic PET, direct reconstruction from projection data is statistically more efficient than conventional indirect methods that perform image reconstruction and kinetic modeling in two separate steps. Existing direct reconstruction methods often use nonlinear compartmental models, which require the knowledge of model order. This paper presents a direct reconstruction approach using a linear spectral representation and does not require model order assumption. A Laplacian prior is used to ensure sparsity in the spectral representation. The resultant maximum a posteriori (MAP) formulation is solved by an expectation maximization shrinkage algorithm. A bias correction step is developed to improve the MAP estimate. Computer simulations show that the proposed method achieves better bias-variance tradeoff than a conventional indirect method for estimating parametric images from dynamic PET data.
Year
DOI
Venue
2009
10.1109/ISBI.2009.5193190
ISBI
Keywords
Field
DocType
expectation-maximisation algorithm,nonlinear compartmental models,projection data,spectral analysis,sparse spectral representation,image reconstruction,pet image direct reconstruction,maximum a posteriori formulation,laplacian prior,tracer kinetic modeling,spectral representation sparsity,positron emission tomography,direct reconstruction,dynamic pet parametric image,dynamic pet,linear spectral representation,dynamic pet parametric images,expectation-maximization shrinkage algorithm,medical image processing,sparse representation,computer simulation,biomedical research,kinetic theory,bioinformatics,pixel,data models,expectation maximization
Iterative reconstruction,Computer vision,Data modeling,Nonlinear system,Pattern recognition,Expectation–maximization algorithm,Computer science,Sparse approximation,Parametric statistics,Pixel,Artificial intelligence,Maximum a posteriori estimation
Conference
Volume
ISSN
ISBN
2009
1945-7928
978-1-4244-3932-4
Citations 
PageRank 
References 
0
0.34
2
Authors
2
Name
Order
Citations
PageRank
Guobao Wang18612.68
Jinyi Qi228435.82