Key result
Lumped parameter bond graph modeling improves cerebral circulation simulation speed ~200-fold while preserving accuracy.
A novel bond graph model of cerebral circulation improves simulation speed by 200-fold while maintaining accuracy, enabling clinically feasible systemic blood flow simulations.
Enables real-time cerebral flow modeling for clinical decision support; leaves open prospective validation before routine adoption.
The primary paper Safaei et al. (2018) proposed an anatomically detailed model of the human cerebral circulation that runs faster than real-time on a desktop computer and is designed for use in clinical settings when the speed of response is important. Based on a one-dimensional formulation of the flow of an incompressible fluid in distensible vessels, a lumped parameter model was developed for 218 arterial segments. The proposed model improved simulation speed by approximately 200-fold while preserved accuracy. Bond graph formulation was used to ensure mass and energy conservation. The model predicted the pressure and flow signatures in the body.
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Su et al. (2020) studied Cerebral circulation modeling. Bond graph model of cerebral circulation was evaluated on Simulation speed and accuracy. A lumped parameter bond graph model of the human cerebral circulation improved simulation speed by approximately 200-fold while preserving accuracy.
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