Title :
Model Estimation of Cerebral Hemodynamics Between Blood Flow and Volume Changes: A Data-Based Modeling Approach
Author :
Wei, Hua-Liang ; Zheng, Ying ; Pan, Yi ; Coca, Daniel ; Li, Liang-Min ; Mayhew, J.E.W. ; Billings, Stephen A.
Author_Institution :
Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, Sheffield
fDate :
6/1/2009 12:00:00 AM
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 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.
Keywords :
autoregressive processes; biomedical MRI; blood; brain; haemodynamics; least squares approximations; neurophysiology; 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; 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; Autoregressive with exogenous input model (ARX); cerebral blood flow (CBF); cerebral blood volume (CBV); parameter estimation; regularization; system identification; total least squares (TLS); Algorithms; Animals; Blood Volume; Cerebrovascular Circulation; Databases, Factual; Hemodynamics; Least-Squares Analysis; Models, Cardiovascular; Rats; Signal Processing, Computer-Assisted;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2009.2012722