Particle filtering for nonlinear BOLD signal analysis

被引：0

作者：

Johnston, Leigh A. ^{[1
]}

Duff, Eugene

Egan, Gary F.

机构：

[1] Howard Florey Inst, Melbourne, Vic, Australia

[2] Ctr Neurosci, Melbourne, Vic, Australia

[3] Univ Melbourne, Dept Elect & Elect Engn, Parkville, Vic 3052, Australia

[4] Univ Melbourne, Dept Math & Stat, Parkville, Vic 3052, Australia

来源：

MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2006, PT 2 | 2006年 / 4191卷

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：292 / 299

页数：8

共 11 条

[1]

Anderson B., 1979, OPTIMAL FILTERING

[2] A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J].