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A computed tomography imaging-based subject-specific whole-lung deposition model

The respiratory tract is an important route for beneficial drug aerosol or harmful particulate matter to enter the body. To assess the therapeutic response or disease risk, whole-lung deposition models have been developed, but were limited by compartment, symmetry or stochastic approaches. In this w...

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Autores principales: Zhang, Xuan, Li, Frank, Rajaraman, Prathish K., Choi, Jiwoong, Comellas, Alejandro P., Hoffman, Eric A., Smith, Benjamin M., Lin, Ching-Long
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477651/
https://www.ncbi.nlm.nih.gov/pubmed/35908637
http://dx.doi.org/10.1016/j.ejps.2022.106272
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author Zhang, Xuan
Li, Frank
Rajaraman, Prathish K.
Choi, Jiwoong
Comellas, Alejandro P.
Hoffman, Eric A.
Smith, Benjamin M.
Lin, Ching-Long
author_facet Zhang, Xuan
Li, Frank
Rajaraman, Prathish K.
Choi, Jiwoong
Comellas, Alejandro P.
Hoffman, Eric A.
Smith, Benjamin M.
Lin, Ching-Long
author_sort Zhang, Xuan
collection PubMed
description The respiratory tract is an important route for beneficial drug aerosol or harmful particulate matter to enter the body. To assess the therapeutic response or disease risk, whole-lung deposition models have been developed, but were limited by compartment, symmetry or stochastic approaches. In this work, we proposed an imaging-based subject-specific whole-lung deposition model. The geometries of airways and lobes were segmented from computed tomography (CT) lung images at total lung capacity (TLC), and the regional air-volume changes were calculated by registering CT images at TLC and functional residual capacity (FRC). The geometries were used to create the structure of entire subject-specific conducting airways and acinar units. The air-volume changes were used to estimate the function of subject-specific ventilation distributions among acinar units and regulate flow rates in respiratory airway models. With the airway dimensions rescaled to a desired lung volume and the airflow field simulated by a computational fluid dynamics model, particle deposition fractions were calculated using deposition probability formulae adjusted with an enhancement factor to account for the effects of secondary flow and airway geometry in proximal airways. The proposed model was validated in silico against existing whole-lung deposition models, three-dimensional (3D) computational fluid and particle dynamics (CFPD) for an acinar unit, and 3D CFPD deep lung model comprising conducting and respiratory regions. The model was further validated in vivo against the lobar particle distribution and the coefficient of variation of particle distribution obtained from CT and single-photon emission computed tomography (SPECT) images, showing good agreement. Subject-specific airway structure increased the deposition fraction of 10.0-μm particles and 0.01-μm particles by approximately 10%. An enhancement factor increased the overall deposition fractions, especially for particle sizes between 0.1 and 1.0 μm.
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spelling pubmed-94776512022-10-01 A computed tomography imaging-based subject-specific whole-lung deposition model Zhang, Xuan Li, Frank Rajaraman, Prathish K. Choi, Jiwoong Comellas, Alejandro P. Hoffman, Eric A. Smith, Benjamin M. Lin, Ching-Long Eur J Pharm Sci Article The respiratory tract is an important route for beneficial drug aerosol or harmful particulate matter to enter the body. To assess the therapeutic response or disease risk, whole-lung deposition models have been developed, but were limited by compartment, symmetry or stochastic approaches. In this work, we proposed an imaging-based subject-specific whole-lung deposition model. The geometries of airways and lobes were segmented from computed tomography (CT) lung images at total lung capacity (TLC), and the regional air-volume changes were calculated by registering CT images at TLC and functional residual capacity (FRC). The geometries were used to create the structure of entire subject-specific conducting airways and acinar units. The air-volume changes were used to estimate the function of subject-specific ventilation distributions among acinar units and regulate flow rates in respiratory airway models. With the airway dimensions rescaled to a desired lung volume and the airflow field simulated by a computational fluid dynamics model, particle deposition fractions were calculated using deposition probability formulae adjusted with an enhancement factor to account for the effects of secondary flow and airway geometry in proximal airways. The proposed model was validated in silico against existing whole-lung deposition models, three-dimensional (3D) computational fluid and particle dynamics (CFPD) for an acinar unit, and 3D CFPD deep lung model comprising conducting and respiratory regions. The model was further validated in vivo against the lobar particle distribution and the coefficient of variation of particle distribution obtained from CT and single-photon emission computed tomography (SPECT) images, showing good agreement. Subject-specific airway structure increased the deposition fraction of 10.0-μm particles and 0.01-μm particles by approximately 10%. An enhancement factor increased the overall deposition fractions, especially for particle sizes between 0.1 and 1.0 μm. 2022-10-01 2022-07-29 /pmc/articles/PMC9477651/ /pubmed/35908637 http://dx.doi.org/10.1016/j.ejps.2022.106272 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Article
Zhang, Xuan
Li, Frank
Rajaraman, Prathish K.
Choi, Jiwoong
Comellas, Alejandro P.
Hoffman, Eric A.
Smith, Benjamin M.
Lin, Ching-Long
A computed tomography imaging-based subject-specific whole-lung deposition model
title A computed tomography imaging-based subject-specific whole-lung deposition model
title_full A computed tomography imaging-based subject-specific whole-lung deposition model
title_fullStr A computed tomography imaging-based subject-specific whole-lung deposition model
title_full_unstemmed A computed tomography imaging-based subject-specific whole-lung deposition model
title_short A computed tomography imaging-based subject-specific whole-lung deposition model
title_sort computed tomography imaging-based subject-specific whole-lung deposition model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477651/
https://www.ncbi.nlm.nih.gov/pubmed/35908637
http://dx.doi.org/10.1016/j.ejps.2022.106272
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