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Proceedings Paper

A novel image analysis method based on Bayesian segmentation for event-related functional MRI
Author(s): Lejian Huang; Mary L. Comer; Thomas M. Talavage
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Paper 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.

Paper Details

Date Published: 26 February 2008
PDF: 10 pages
Proc. SPIE 6814, Computational Imaging VI, 68140D (26 February 2008); doi: 10.1117/12.774977
Show Author Affiliations
Lejian Huang, Purdue Univ. (United States)
Mary L. Comer, Purdue Univ. (United States)
Thomas M. Talavage, Purdue Univ. (United States)
Indiana Univ. School of Medicine (United States)

Published in SPIE Proceedings Vol. 6814:
Computational Imaging VI
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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