A novel image analysis method based on bayesian segmentation for event-related functional MRI

Authors

Lejian Huang
Mary L. Comer
Thomas M. Talavage

Abstract

This paper presents the application of the expectation-maximization/ maximization of the posterior marginals (EM/MPM) algorithm to signal detection for functional MRI (fMRI). On basis of assumptions for fMRI 3-D image data, a novel analysis method is proposed and applied to synthetic data and human brain data. Synthetic data analysis is conducted using two statistical noise models (white and autoregressive of order 1) and, for low contrast-to-noise ratio (CNR) data, reveals better sensitivity and specificity for the new method than for the traditional General Linear Model (GLM) approach. When applied to human brain data, functional activation regions are found to be consistent with those obtained using the GLM approach.

Comments

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Keywords

Brain, Computational methods, Data reduction, Image analysis, Imaging systems, Imaging techniques, Mathematical models, Microfluidics, Modal analysis, Photoresists, Sensitivity analysis, Signal processing, Statistical methods, Three dimensional, Vegetation

Date of this Version

January 2008

DOI

http://dx.doi.org/10.1117/12.774977

Published in:

Proceedings of SPIE - The International Society for Optical Engineering 6814,(2008) The Society for Imaging Science and Technology (IS and T); The International Society for Optical Engineering (SPIE); GE Healthcare-The Society for Imaging Science and Technology (IS and T); The International Society for Optical Engineering (SPIE); GE Healthcare;