Probabilistic modeling of single-trial fMRI data

Author

Svensén, Markus ; Kruggel, Frithjof ; Von Cramon, D. Yves

Author_Institution

Max-Planck Inst. of Cognitive Neurosci., Leipzig, Germany

Volume

19

Issue

1

fYear

2000

Firstpage

25

Lastpage

35

Abstract

Describes a probabilistic framework for modeling single-trial functional magnetic resonance (fMR) images based on a parametric model for the hemodynamic response and Markov random field (MRF) image models. The model is fitted to image data by maximizing a lower bound on the log likelihood. The result is an approximate maximum a posteriori estimate of the joint distribution over the model parameters and pixel labels. Examples show how this technique can used to segment two-dimensional (2-D) fMR images, or parts thereof, into regions with different characteristics of their hemodynamic response.

Keywords

biomedical MRI; brain models; haemodynamics; image segmentation; medical image processing; Markov random field image models; approximate maximum a posteriori estimate; hemodynamic response; joint distribution; log likelihood; model parameters; parametric model; pixel labels; probabilistic modeling; single-trial fMRI data; single-trial functional magnetic resonance images; Blood; Delay; Hemodynamics; Image segmentation; Magnetic field measurement; Magnetic resonance; Markov random fields; Parameter estimation; Parametric statistics; Performance evaluation; Brain; Computer Simulation; Hemodynamics; Humans; Image Processing, Computer-Assisted; Likelihood Functions; Magnetic Resonance Imaging; Markov Chains; Models, Statistical; Phantoms, Imaging;

fLanguage

English

Journal_Title

Medical Imaging, IEEE Transactions on

Publisher

ieee

ISSN

0278-0062

Type

jour

DOI

10.1109/42.832957

Filename

832957