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

Lejian Huang; Mary L. Comer; Thomas M. Talavage

doi:10.1117/12.774977

26 February 2008 A novel image analysis method based on Bayesian segmentation for event-related functional MRI

Lejian Huang, Mary L. Comer, Thomas M. Talavage

Proceedings Volume 6814, Computational Imaging VI; 68140D (2008) https://doi.org/10.1117/12.774977
Event: Electronic Imaging, 2008, San Jose, California, United States

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.

Citation Download Citation

Lejian Huang, Mary L. Comer, and Thomas M. Talavage "A novel image analysis method based on Bayesian segmentation for event-related functional MRI", Proc. SPIE 6814, Computational Imaging VI, 68140D (26 February 2008); https://doi.org/10.1117/12.774977

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