abstract bibtex

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.

@article{wei_model_2009, title = {Model {Estimation} of {Cerebral} {Hemodynamics} {Between} {Blood} {Flow} and {Volume} {Changes}: {A} {Data}-{Based} {Modeling} {Approach}}, volume = {56}, issn = {0018-9294}, shorttitle = {Model {Estimation} of {Cerebral} {Hemodynamics} {Between} {Blood} {Flow} and {Volume} {Changes}}, 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.}, number = {6}, journal = {Biomedical Engineering, IEEE Transactions on}, author = {Wei, H.-L. and Zheng, Y. and Pan, Y. and Coca, D. and Li, L.-M. and Mayhew, J. E. W. and Billings, S. A.}, year = {2009}, keywords = {theory, CBF, BOLD, balloonmodel, CBV, analysis}, pages = {1606--1616}, file = {wei2009.pdf:/Users/nickb/Zotero/storage/2E6GG8PA/wei2009.pdf:application/pdf} }

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