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Proceedings Paper

Parameterization of motion artifacts in fMRI time series using autoregressive models for the construction of computer-generated phantoms
Author(s): Yong Li; Victoria L. Morgan; David R. Pickens; Benoit M. Dawant
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Paper Abstract

We explore the use of scalar and multivariate autoregressive models to parameterize motion artifacts in fMRI time series. To do so, we acquire real fMRI data sets, measure rigid body motion in these data sets, and classify the type of observed motion in several categories such as random motion or motion correlated with activation. The measured motion sequences are then modeled and used to generate realistic image phantoms that can be used to validate fMRI data analysis packages. We compare phantoms generated with the original motion sequences and phantoms generated with simulated sequences. We show that both scalar and multivariate autoregressive models can be used to generate realistic motion sequences. An important difference between the two is the fact that multivariate models can capture correlations between motion parameters, which cannot be done with scalar models.

Paper Details

Date Published: 13 March 2006
PDF: 8 pages
Proc. SPIE 6143, Medical Imaging 2006: Physiology, Function, and Structure from Medical Images, 61431U (13 March 2006); doi: 10.1117/12.653580
Show Author Affiliations
Yong Li, Vanderbilt Univ. (United States)
Victoria L. Morgan, Vanderbilt Univ. (United States)
David R. Pickens, Vanderbilt Univ. (United States)
Benoit M. Dawant, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 6143:
Medical Imaging 2006: Physiology, Function, and Structure from Medical Images
Armando Manduca; Amir A. Amini, Editor(s)

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