Name
Abstract | ||
|---|---|---|
We introduce a 4-dimensional joint generative probabilistic model for estimation of activity in a PET/MRI imaging system. The model is based on a mixture of Gaussians, relating time dependent activity and MRI image intensity to a hidden static variable, allowing one to estimate jointly activity, the parameters that capture the interdependence of the two images and motion parameters. An iterative algorithm for optimisation of the model is described. Noisy simulation data, modeling 3-D patient head movements, is obtained with realistic PET and MRI simulators and with a brain phantom from the BrainWeb database. Joint estimation of activity and motion parameters within the same framework allows us to use information from the MRI images to improve the activity estimate in terms of noise and recovery. |
Year | Venue | Keywords |
|---|---|---|
2011 | MICCAI | 4-dimensional joint generative probabilistic,4-d generative model,mri simulator,motion parameter,mri imaging system,time dependent activity,realistic pet,joint estimation,mri image intensity,activity estimate,mri reconstruction,mri image,molecular imaging,bayesian networks |
Field | DocType | Volume |
Computer vision,Pattern recognition,Computer science,Iterative method,Imaging phantom,Image processing,Bayesian network,Statistical model,Artificial intelligence,Mixture model,Magnetic resonance imaging,Generative model | Conference | 14 |
Issue | ISSN | Citations |
Pt 1 | 0302-9743 | 5 |
PageRank | References | Authors |
0.55 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefano Pedemonte | 1 | 63 | 6.80 |
| Alexandre Bousse | 2 | 8 | 2.96 |
| Brian F. Hutton | 3 | 98 | 14.33 |
| Simon Arridge | 4 | 47 | 4.64 |
| Sébastien Ourselin | 5 | 576 | 57.16 |