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Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modelling approach

Wei, H.L., Zheng, Y., Pan, Y., Coca, D., Li, L.M., Mayhew, J.E.W. and Billings, S.A. (2008) Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modelling approach. Research Report. ACSE Research Report no. 983 . Automatic Control and Systems Engineering, University of Sheffield

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Abstract

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional magnetic resonance imaging (fMRI), where the blood oxygen level dependent (BOLD) signals are recorded, the understanding and accurate modelling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modelling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model (ARX) structure. It is observed that neither the ordinary least squares (LS) method nor the classical total least squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data, in that the resultant ARX models may be unstable and thus cannot generate stable model predicted outputs. A regularized total least squares (RTLS) method is employed and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV.

Item Type: Monograph (Research Report)
Copyright, Publisher and Additional Information: The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
Keywords: cerebral blood flow, cerebral blood volume, system identification, parameter estimation, nonlinear autoregressive with exogenous input model, total least squares, regularization.
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) > ACSE Research Reports
Depositing User: Miss Anthea Tucker
Date Deposited: 16 Oct 2012 08:58
Last Modified: 07 Jun 2014 08:41
Status: Published
Publisher: Automatic Control and Systems Engineering, University of Sheffield
URI: http://eprints.whiterose.ac.uk/id/eprint/74639

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