Dataset Information

A Numerical Study of Water Loss Rate Distributions in MDCT-Based Human Airway Models.

ABSTRACT: Both three-dimensional (3D) and one-dimensional (1D) computational fluid dynamics methods are applied to study regional water loss in three multi-detector row computed-tomography-based human airway models at the minute ventilations of 6, 15 and 30 L/min. The overall water losses predicted by both 3D and 1D models in the entire respiratory tract agree with available experimental measurements. However, 3D and 1D models reveal different regional water loss rate distributions due to the 3D secondary flows formed at bifurcations. The secondary flows cause local skewed temperature and humidity distributions on inspiration acting to elevate the local water loss rate; and the secondary flow at the carina tends to distribute more cold air to the lower lobes. As a result, the 3D model predicts that the water loss rate first increases with increasing airway generation, and then decreases as the air approaches saturation, while the 1D model predicts a monotonic decrease of water loss rate with increasing airway generation. Moreover, the 3D (or 1D) model predicts relatively higher water loss rates in lower (or upper) lobes. The regional water loss rate can be related to the non-dimensional wall shear stress (? (*)) by the non-dimensional mass transfer coefficient (h 0 (*) ) as [Formula: see text].

SUBMITTER: Wu D

PROVIDER: S-EPMC4605848 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Numerical Study of Water Loss Rate Distributions in MDCT-Based Human Airway Models.

Wu Dan D Miyawaki Shinjiro S Tawhai Merryn H MH Hoffman Eric A EA Lin Ching-Long CL

Annals of biomedical engineering 20150414 11

Both three-dimensional (3D) and one-dimensional (1D) computational fluid dynamics methods are applied to study regional water loss in three multi-detector row computed-tomography-based human airway models at the minute ventilations of 6, 15 and 30 L/min. The overall water losses predicted by both 3D and 1D models in the entire respiratory tract agree with available experimental measurements. However, 3D and 1D models reveal different regional water loss rate distributions due to the 3D secondary ...[more]

PMID: 25869455

Similar Datasets

Project description:ObjectivesQuantitative computed tomography (QCT) offers some promising markers to quantify cystic fibrosis (CF)-lung disease. Air trapping may precede irreversible bronchiectasis; therefore, the temporal interdependencies of functional and structural lung disease need to be further investigated. We aim to quantify airway dimensions and air trapping on chest CT of school-age children with mild CF-lung disease over two years.MethodsFully-automatic software analyzed 144 serial spirometer-controlled chest CT scans of 36 children (median 12.1 (10.2-13.8) years) with mild CF-lung disease (median ppFEV1 98.5 (90.8-103.3) %) at baseline, 3, 12 and 24 months. The airway wall percentage (WP5-10), bronchiectasis index (BEI), as well as severe air trapping (A3) were calculated for the total lung and separately for all lobes. Mixed linear models were calculated, considering the lobar distribution of WP5-10, BEI and A3 cross-sectionally and longitudinally.ResultsWP5-10 remained stable (P = 0.248), and BEI changed from 0.41 (0.28-0.7) to 0.54 (0.36-0.88) (P = 0.156) and A3 from 2.26% to 4.35% (P = 0.086) showing variability over two years. ppFEV1 was also stable (P = 0.276). A robust mixed linear model showed a cross-sectional, regional association between WP5-10 and A3 at each timepoint (P < 0.001). Further, BEI showed no cross-sectional, but another mixed model showed short-term longitudinal interdependencies with air trapping (P = 0.003).ConclusionsRobust linear/beta mixed models can still reveal interdependencies in medical data with high variability that remain hidden with simpler statistical methods. We could demonstrate cross-sectional, regional interdependencies between wall thickening and air trapping. Further, we show short-term regional interdependencies between air trapping and an increase in bronchiectasis. The data indicate that regional air trapping may precede the development of bronchiectasis. Quantitative CT may capture subtle disease progression and identify regional and temporal interdependencies of distinct manifestations of CF-lung disease.

Project description:BackgroundCell-based models are becoming increasingly popular for applications in developmental biology. However, the impact of numerical choices on the accuracy and efficiency of the simulation of these models is rarely meticulously tested. Without concrete studies to differentiate between solid model conclusions and numerical artifacts, modelers are at risk of being misled by their experiments' results. Most cell-based modeling frameworks offer a feature-rich environment, providing a wide range of biological components, but are less suitable for numerical studies. There is thus a need for software specifically targeted at this use case.ResultsWe present CBMOS, a Python framework for the simulation of the center-based or cell-centered model. Contrary to other implementations, CBMOS' focus is on facilitating numerical study of center-based models by providing access to multiple ordinary differential equation solvers and force functions through a flexible, user-friendly interface and by enabling rapid testing through graphics processing unit (GPU) acceleration. We show-case its potential by illustrating two common workflows: (1) comparison of the numerical properties of two solvers within a Jupyter notebook and (2) measuring average wall times of both solvers on a high performance computing cluster. More specifically, we confirm that although for moderate accuracy levels the backward Euler method allows for larger time step sizes than the commonly used forward Euler method, its additional computational cost due to being an implicit method prohibits its use for practical test cases.ConclusionsCBMOS is a flexible, easy-to-use Python implementation of the center-based model, exposing both basic model assumptions and numerical components to the user. It is available on GitHub and PyPI under an MIT license. CBMOS allows for fast prototyping on a central processing unit for small systems through the use of NumPy. Using CuPy on a GPU, cell populations of up to 10,000 cells can be simulated within a few seconds. As such, it will substantially lower the time investment for any modeler to check the crucial assumption that model conclusions are independent of numerical issues.

Dataset Information

A Numerical Study of Water Loss Rate Distributions in MDCT-Based Human Airway Models.

Publications

A Numerical Study of Water Loss Rate Distributions in MDCT-Based Human Airway Models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets