By Topic

An analysis of Blood-Oxygen-Level-Dependent signal parameter estimation using particle filters

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Chambers, M. ; Bradley Dept. of Electr. & Comput. Eng., Virginia Tech, Blacksburg, VA, USA ; Wyatt, C.

The Blood-Oxygen-Level-Dependent (BOLD) signal that is measured by functional magnetic resonance imaging (fMRI) has been the subject of extensive research since the development of the first balloon model. While there are definite benefits to moving from the Canonical Hemodynamic Response function to a physiologically inspired BOLD model, significant barriers remain. Optimizing the simplest balloon model requires searching within 7 dimensions, and even more complex models exist. Additionally, the nonlinear nature of these models make them difficult to analyze; therefore, this work uses a particle filter to regresses a simple form of the BOLD model. Whereas traditional methods of analyzing fMRI aims to determine where activation occurs, BOLD model regression seeks a parametric representation of the signal. The results show that the particle filter attains a good fit but that the system of equations are not observable, leading to a large range of parameters that are consistent with the measurements.

Published in:

Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on

Date of Conference:

March 30 2011-April 2 2011