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Proceedings Paper

Nonparametric suppression of random and physiological noise components in functional magnetic resonance imaging using cross-correlation spectrum subtraction
Author(s): Tamer Youssef; Abou-Bakr M. Youssef; Stephen M. LaConte; Xiaoping P. Hu; Yasser M. Kadah
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Paper Abstract

The advent of event-related functional magnetic resonance imaging (fMRI) has resulted in many exciting studies that have exploited its unique capability. However, the utility of event-related fMRI is still limited by several technical difficulties. One significant limitation in event-related fMRI is the low signal-to-noise ratio (SNR). In this work, a new non-parametric technique for noise suppression in event related fMRI data is proposed based on spectrum subtraction. The new technique is based on generalized spectral subtraction that allows correlated noise components to be treated robustly. Moreover, it adaptively estimates a nonparametric model for random and physiological components of noise from the acquired data in a simple and computationally efficient manner. This allows the new method to overcome the limitations of previous methods while maintaining a robust performance given its fewer assumptions and suggests its value as a useful preprocessing step for fMRI data analysis.

Paper Details

Date Published: 2 May 2003
PDF: 8 pages
Proc. SPIE 5031, Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications, (2 May 2003); doi: 10.1117/12.480688
Show Author Affiliations
Tamer Youssef, Cairo Univ. (Egypt)
Abou-Bakr M. Youssef, Cairo Univ. (Egypt)
Stephen M. LaConte, Emory Univ. (United States)
Georgia Institute of Technology (United States)
Xiaoping P. Hu, Emory Univ. (United States)
Georgia Institute of Technology (United States)
Yasser M. Kadah, Cairo Univ. (Egypt)
Emory Univ. (United States)
Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 5031:
Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications
Anne V. Clough; Amir A. Amini, Editor(s)

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