Probabilistic modeling of single-trial fMRI data

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 resul...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2000-01, Vol.19 (1), p.25-35
Main Authors: Svensen, M., Kruggel, F., von Cramon, D.Y.
Format: Article
Language:English
Subjects:
Biological and medical sciences
Blood
Brain - blood supply
Brain - pathology
Computer Simulation
Delay
Hemodynamics
Hemodynamics - physiology
Humans
Image Processing, Computer-Assisted
Image segmentation
Investigative techniques, diagnostic techniques (general aspects)
Likelihood Functions
Magnetic field measurement
Magnetic resonance
Magnetic Resonance Imaging - statistics & numerical data
Markov Chains
Markov processes
Markov random fields
Mathematical models
Maximum likelihood estimation
Medical sciences
Models, Statistical
Nervous system
Parameter estimation
Parametric statistics
Performance evaluation
Phantoms, Imaging
Probability
Radiodiagnosis. Nmr imagery. Nmr spectrometry
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Summary: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.
ISSN:0278-0062
1558-254X
DOI:10.1109/42.832957