Blood flow distribution in an anatomically detailed arterial network model: criteria and algorithms View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-11

AUTHORS

Pablo J. Blanco, Sansuke M. Watanabe, Enzo A. Dari, Marco Aurélio R. F. Passos, Raúl A. Feijóo

ABSTRACT

Development of blood flow distribution criteria is a mandatory step toward developing computational models and numerical simulations of the systemic circulation. In the present work, we (i) present a systematic approach based on anatomical and physiological considerations to distribute the blood flow in a 1D anatomically detailed model of the arterial network and (ii) develop a numerical procedure to calibrate resistive parameters in terminal models in order to effectively satisfy such flow distribution. For the first goal, we merge data collected from the specialized medical literature with anatomical concepts such as vascular territories to determine blood flow supply to specific (encephalon, kidneys, etc.) and distributed (muscles, skin, etc.) organs. Overall, 28 entities representing the main specific organs are accounted for in the detailed description of the arterial topology that we use as model substrate. In turn, 116 vascular territories are considered as the basic blocks that compose the distributed organs throughout the whole body. For the second goal, Windkessel models are used to represent the peripheral beds, and the values of the resistive parameters are computed applying a Newton method to a parameter identification problem to guarantee the supply of the correct flow fraction to each terminal location according to the given criteria. Finally, it is shown that, by means of the criteria developed, and for a rather standard set of model parameters, the model predicts physiologically realistic pressure and flow waveforms. More... »

PAGES

1303-1330

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10237-014-0574-8

    DOI

    http://dx.doi.org/10.1007/s10237-014-0574-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1013550670

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/24682727


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