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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, 56 (6). pp. 1606-1616. ISSN 0018-9294

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To link to this item DOI: 10.1109/TBME.2009.2012722

Abstract/Summary

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
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:33481
Uncontrolled Keywords:blood oxygen-level-dependent signal; blood volume change; cerebral blood flow; cerebral hemodynamics; data-based modeling framework; error-in-the-variables problem; filtering method; functional MRI; least-squares method; parsimonious autoregressive analysis; Autoregressive with exogenous input model (ARX); cerebral blood flow (CBF); cerebral blood volume (CBV); parameter estimation; regularization; system identification; total least squares (TLS); Blood flow; Filtering; Hemodynamics; Least squares approximation; Least squares methods; Magnetic resonance imaging; Mathematical model; Parameter estimation; Positron emission tomography; Power system modeling; System identification; Systems engineering and theory; Algorithms; Animals; Blood Volume; Cerebrovascular Circulation; Databases, Factual; Hemodynamics; Least-Squares Analysis; Models, Cardiovascular; Rats; Signal Processing, Computer-Assisted
Publisher:IEEE

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