Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modeling approach.
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.
Wei
Hua-Liang
HL
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK. w.Hualiang@sheffield.ac.uk
Zheng
Ying
Y
Pan
Yi
Y
Coca
Daniel
D
Li
Liang-Min
LM
Mayhew
J E W
JE
Billings
Stephen A
SA
D000465:Algorithms; D000818:Animals; D001810:Blood Volume / Q000502:physiology*; D002560:Cerebrovascular Circulation / Q000502:physiology*; D016208:Databases, Factual; D006439:Hemodynamics / Q000502:physiology*; D016018:Least-Squares Analysis; D008955:Models, Cardiovascular; D051381:Rats; D012815:Signal Processing, Computer-Assisted