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

Wei, H.L., Zheng, Y., Pan, Y., Coca, D., Li, L.M., Mayhew, J.E.W. and Billings, S.A. (2009) Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modeling approach. IEEE Transactions on Biomedical Engineering Bme, 56 (6). pp. 1606-1616. ISSN 0018-9294


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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 MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling 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 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. A regularized total least-squares (RTLS) method is thus introduced 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: Article
Copyright, Publisher and Additional Information: © Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: Autoregressive with exogenous input model (ARX); cerebral blood flow (CBF); cerebral blood volume (CBV); parameter estimation; regularization; system identification; total least squares (TLS)
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield)
The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Miss Anthea Tucker
Date Deposited: 06 Jul 2009 09:22
Last Modified: 09 Jun 2014 02:22
Published Version: http://dx.doi.org/10.1109/TBME.2009.2012722
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: 10.1109/TBME.2009.2012722
URI: http://eprints.whiterose.ac.uk/id/eprint/8747

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