Particle filtering for nonlinear BOLD signal analysis

Leigh A. Johnston, Eugene Duff, Gary F. Egan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2006
Subtitle of host publication9th International Conference Copenhagen, Denmark, October 1-6, 2006 Proceedings, Part II
EditorsRasmus Larsen, Mads Nielsen, Jon Sporring
Place of PublicationBerlin Germany
Number of pages8
ISBN (Print)354044727X, 9783540447276
Publication statusPublished - 1 Dec 2006
Externally publishedYes
EventMedical Image Computing and Computer-Assisted Intervention 2006 - Copenhagen, Denmark
Duration: 1 Oct 20066 Oct 2006
Conference number: 9th (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


ConferenceMedical Image Computing and Computer-Assisted Intervention 2006
Abbreviated titleMICCAI 2006
Internet address

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