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- W2022309414 abstract "Detailed description of the flow field in human airways is highly important to better understand human breathing and provide a patient's customized diagnosis. An integrated numerical simulation platform is presently proposed in order to incorporate medical images into a numerical software to calculate flow field and to analyze it in terms of fluid dynamics. The platform was set up to compute steady inspiratory airflow in realistic human airways reconstructed from tomodensitometric medical images at resting breathing conditions. This morpho-functional simulation platform has been tested retrospectively with two CT-scanned patient airway morphological models: (i) a normal airway model (subject A) with no evidence of morphological alteration and (ii) a highly altered airway model (subject B) exhibiting a severe stenosis in the right main bronchus. First, various morphological aspects proper to each airway model are provided to show the performance and interest of the reconstruction method. Second, we describe the three-dimensional flow patterns associated to the global morphological features, which are mainly shared by the present realistic models and previous idealistic airway models. Finally, the flow characteristics associated to local morphological features specific to realistic airway models are discussed. The results demonstrate that the morpho-functional simulation platform is able to capture the main features of airway velocity patterns but also more specific airflow patterns which are related to customized patient morphological features such as laminar vortex formation. The present results suggest that the proposed airway functional imaging platform is adequate to provide most of functional information related to airflow and enable a patient to patient diagnosis." @default.
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- W2022309414 date "2005-08-01" @default.
- W2022309414 modified "2023-10-18" @default.
- W2022309414 title "Airflow modeling of steady inspiration in two realistic proximal airway trees reconstructed from human thoracic tomodensitometric images" @default.
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- W2022309414 doi "https://doi.org/10.1080/10255840500289772" @default.
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