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

F Y Wu1, J D Slater

  • 1Department of Electrical and Computer Engineering, University of Miami, Coral Gables 33124.

International Journal of Bio-Medical Computing
|September 1, 1993
PubMed
Summary

This study introduces an artificial neural network (ANN) for real-time regional cerebral blood flow (rCBF) estimation. This simplified ANN approach offers a generalized method for parametric regression analysis beyond rCBF.

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Area of Science:

  • Biomedical Engineering
  • Computational Neuroscience
  • Medical Imaging

Background:

  • Accurate estimation of regional cerebral blood flow (rCBF) is crucial for diagnosing and monitoring neurological conditions.
  • Conventional methods for rCBF estimation, often relying on curve fitting strategies, can be complex and time-consuming.

Purpose of the Study:

  • To develop and validate an artificial neural network (ANN) model for the real-time estimation of regional cerebral blood flow (rCBF).
  • To introduce a systematic methodology for applying ANNs to parametric regression analysis.

Main Methods:

  • An artificial neural network (ANN) was designed based on a regression model described by a linear differential equation.
  • The ANN was trained using head and expired air curves obtained from 133Xe inhalation data.

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  • A design-oriented methodology was employed, where ANN weights directly correspond to the parameters of the best-fit regression model.

    • Main Results:

    • The proposed ANN model accurately estimated rCBF in real-time.
    • The ANN approach significantly simplified the parameter estimation process compared to conventional curve fitting strategies.
    • Experimental results demonstrated strong agreement between the ANN method and traditional techniques.

    Conclusions:

    • Artificial neural networks offer a powerful and simplified tool for real-time parametric regression analysis, exemplified by rCBF estimation.
    • The introduced design-oriented methodology for ANN development extends their applicability beyond classification tasks.
    • This approach holds potential for generalized applications in various scientific and medical fields requiring parametric modeling.