Particle filtering for nonlinear BOLD signal analysis

Authors:
Leigh A. Johnston;Eugene Duff;Gary F. Egan
Affiliations:
Howard Florey Institute & Centre for Neuroscience, Melbourne, Australia;Howard Florey Institute & Centre for Neuroscience, Melbourne, Australia;Howard Florey Institute & Centre for Neuroscience, Melbourne, Australia
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2006

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Abstract

Functional Magnetic Resonance imaging studies analyse sequences of brain volumes whose intensity changes predominantly reflect blood oxygenation level dependent (BOLD) effects. The most comprehensive signal model to date of the BOLD effect is formulated as a continuous-time system of nonlinear stochastic differential equations. In this paper we present a particle filtering method for the analysis of the BOLD system, and demonstrate it to be both accurate and robust in estimating the hidden physiological states including cerebral blood flow, cerebral blood volume, total deoxyhemoglobin content, and the flow inducing signal, from functional imaging data.