Paper Info
Title
Spatio-temporal Covariance Model for Medical Images Sequences: Application to Functional MRI Data
Abstract
Spatial and temporal correlations which affect the signal measured in functional MRI (fMRI) are usually not considered simultaneously (i.e., as non-independent random processes) in statistical methods dedicated to detecting cerebral activation. We propose a new method for modeling the covariance of a stationary spatio-temporal random process and apply this approach to fMRI data analysis. For doing so, we introduce a multivariate regression model which takes simultaneously the spatial and temporal correlations into account. We show that an experimental variogram of the regression error process can be fitted to a valid nonseparable spatio-temporal covariance model. This yields a more robust estimation of the intrinsic spatio-temporal covariance of the error process and allows a better modeling of the properties of the random fluctuations affecting the hemodynamic signal. The practical relevance of our model is illustrated using real event-related fMRI experiments.
Year
DOI
Venue
2001
10.1007/3-540-45729-1_20
IPMI
Keywords
Field
DocType
regression error process,random fluctuation,real event-related fmri experiment,spatio-temporal covariance model,medical images sequences,functional mri data,non-independent random process,error process,temporal correlation,multivariate regression model,valid nonseparable spatio-temporal covariance,stationary spatio-temporal random process,intrinsic spatio-temporal covariance,data analysis,random process,multivariate regression,robust estimator
Variogram,Covariance function,Estimation of covariance matrices,Pattern recognition,Multivariate statistics,Computer science,Stochastic process,Image processing,Statistical model,Artificial intelligence,Covariance
Conference
ISBN
Citations 
PageRank 
3-540-42245-5
5
2.21
References 
Authors
3
3
Name
Order
Citations
PageRank
Habib Benali183768.94
Mélanie Pélégrini-Issac227521.68
Frithjof Kruggel344877.26