Paper Info
Title
Probabilistic graphical model of SPECT/MRI
Abstract
The combination of PET and SPECT with MRI is an area of active research at present time and will enable new biological and pathological analysis tools for clinical applications and pre-clinical research. Image processing and reconstruction in multi-modal PET/MRI and SPECT/MRI poses new algorithmic and computational challenges. We investigate the use of Probabilistic Graphical Models (PGM) to construct a system model and to factorize the complex joint distribution that arises from the combination of the two imaging systems. A joint generative system model based on finite mixtures is proposed and the structural properties of the associated PGM are addressed in order to obtain an iterative algorithm for estimation of activity and multimodal segmentation. In a SPECT/MRI digital phantom study, the proposed algorithm outperforms a well established method for multi-modal activity estimation in terms of bias/variance characteristics and identification of lesions.
Year
Venue
Keywords
2011
MLMI
active research,complex joint distribution,probabilistic graphical model,multi-modal pet,multi-modal activity estimation,imaging system,joint generative system model,iterative algorithm,mri digital phantom study,new algorithmic,associated pgm,bayesian networks,molecular imaging
Field
DocType
Volume
Iterative reconstruction,Computer vision,Pattern recognition,Segmentation,Computer science,Imaging phantom,Image processing,Bayesian network,Artificial intelligence,Graphical model,Probabilistic logic,Real-time MRI
Conference
7009
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
4
5
Name
Order
Citations
PageRank
Stefano Pedemonte1636.80
Alexandre Bousse282.96
Brian F. Hutton39814.33
Simon Arridge4474.64
Sébastien Ourselin557657.16