Investigation of tracer gas transport in a new numerical model of lung acini

Obstructive pulmonary diseases are associated with considerable morbidity. For an early diagnosis of these diseases, inert gas washouts can potentially be used. However, the complex interaction between lung anatomy and gas transport mechanisms complicates data analysis. In order to investigate this...

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Hauptverfasser: Schmidt, Christoph (VerfasserIn) , Joppek, Christoph (VerfasserIn) , Trinkmann, Frederik (VerfasserIn) , Takors, Ralf (VerfasserIn) , Cattaneo, Giorgio (VerfasserIn) , Port, Johannes (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 06 July 2022
In: Medical & biological engineering & computing
Year: 2022, Jahrgang: 60, Heft: 9, Pages: 2619-2637
ISSN:1741-0444
DOI:10.1007/s11517-022-02608-x
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s11517-022-02608-x
Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1007/s11517-022-02608-x
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Verfasserangaben:Christoph Schmidt, Christoph Joppek, Frederik Trinkmann, Ralf Takors, Giorgio Cattaneo, Johannes Port

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520 |a Obstructive pulmonary diseases are associated with considerable morbidity. For an early diagnosis of these diseases, inert gas washouts can potentially be used. However, the complex interaction between lung anatomy and gas transport mechanisms complicates data analysis. In order to investigate this interaction, a numerical model, based on the finite difference method, consisting of two lung units connected in parallel, was developed to simulate the tracer gas transport within the human acinus. Firstly, the geometries of the units were varied and the diffusion coefficients (D) were kept constant. Secondly, D was changed and the geometry was kept constant. Furthermore, simple monoexponential growth functions were applied to evaluate the simulated data. In 109 of the 112 analyzed curves, monoexponential function matched simulated data with an accuracy of over 90%, potentially representing a suitable numerical tool to predict transport processes in further model extensions. For total flows greater than 5 × 10−4 ml/s, the exponential growth constants increased linearly with linear increasing flow to an accuracy of over 95%. The slopes of these linear trend lines of 1.23 µl−1 (D = 0.6 cm2/s), 1.69 µl−1 (D = 0.3 cm2/s), and 2.25 µl−1 (D = 0.1 cm2/s) indicated that gases with low D are more sensitive to changes in flows than gases with high D. 
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