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Regional cerebral blood flow estimation by neural network-based parametric regression analysis.

F Y Wu·J D Slater
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Abstract

An artificial neural network (ANN) model was proposed for real-time estimation of regional cerebral blood flow (rCBF), by given head and expired air curves obtained through 133Xe inhalation. The network was constructed according to a regression model described by a linear differential equation. Experimental results compare well with those obtained by conventional curve fitting strategies, but the parameter estimation process is much simplified. A systematic procedure in developing ANN for parametric regression analysis was introduced; networks are constructed according to the selected regression model so that the obtained weights of a trained network directly represent parameters of the regression model which best fits the observed data set. Such a design-oriented methodology extends the classification-based applications of ANN to parametric regression analysis, and therefore may have more generalized applications besides rCBF estimation.

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AlgorithmsCerebrovascular CirculationCerebrovascular DisordersHumansMathematical ComputingModels, NeurologicalNeural Networks, ComputerReference ValuesRegression AnalysisJournal ArticleResearch Support, Non-U.S. Gov't

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BibTeXRIS

F Y Wu, J D Slater. 1993. Regional cerebral blood flow estimation by neural network-based parametric regression analysis.. https://doi.org/10.1016/0020-7101(93)90029-6

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