Human upper-airway respiratory airflow: In vivo comparison of computational fluid dynamics simulations and hyperpolarized 129Xe phase contrast MRI velocimetry
Computational fluid dynamics (CFD) simulations of respiratory airflow have the potential to change the clinical assessment of regional airway function in health and disease, in pulmonary medicine and otolaryngology. For example, in diseases where multiple sites of airway obstruction occur, such as o...
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| Vydané v: | PloS one Ročník 16; číslo 8; s. e0256460 |
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| Hlavní autori: | , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
Public Library of Science
19.08.2021
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Computational fluid dynamics (CFD) simulations of respiratory airflow have the potential to change the clinical assessment of regional airway function in health and disease, in pulmonary medicine and otolaryngology. For example, in diseases where multiple sites of airway obstruction occur, such as obstructive sleep apnea (OSA), CFD simulations can identify which sites of obstruction contribute most to airway resistance and may therefore be candidate sites for airway surgery. The main barrier to clinical uptake of respiratory CFD to date has been the difficulty in validating CFD results against a clinical gold standard. Invasive instrumentation of the upper airway to measure respiratory airflow velocity or pressure can disrupt the airflow and alter the subject’s natural breathing patterns. Therefore, in this study, we instead propose phase contrast (PC) velocimetry magnetic resonance imaging (MRI) of inhaled hyperpolarized
129
Xe gas as a non-invasive reference to which airflow velocities calculated via CFD can be compared. To that end, we performed subject-specific CFD simulations in airway models derived from
1
H MRI, and using respiratory flowrate measurements acquired synchronously with MRI. Airflow velocity vectors calculated by CFD simulations were then qualitatively and quantitatively compared to velocity maps derived from PC velocimetry MRI of inhaled hyperpolarized
129
Xe gas. The results show both techniques produce similar spatial distributions of high velocity regions in the anterior-posterior and foot-head directions, indicating good qualitative agreement. Statistically significant correlations and low Bland-Altman bias between the local velocity values produced by the two techniques indicates quantitative agreement. This preliminary
in vivo
comparison of respiratory airway CFD and PC MRI of hyperpolarized
129
Xe gas demonstrates the feasibility of PC MRI as a technique to validate respiratory CFD and forms the basis for further comprehensive validation studies. This study is therefore a first step in the pathway towards clinical adoption of respiratory CFD. |
|---|---|
| AbstractList | Computational fluid dynamics (CFD) simulations of respiratory airflow have the potential to change the clinical assessment of regional airway function in health and disease, in pulmonary medicine and otolaryngology. For example, in diseases where multiple sites of airway obstruction occur, such as obstructive sleep apnea (OSA), CFD simulations can identify which sites of obstruction contribute most to airway resistance and may therefore be candidate sites for airway surgery. The main barrier to clinical uptake of respiratory CFD to date has been the difficulty in validating CFD results against a clinical gold standard. Invasive instrumentation of the upper airway to measure respiratory airflow velocity or pressure can disrupt the airflow and alter the subject's natural breathing patterns. Therefore, in this study, we instead propose phase contrast (PC) velocimetry magnetic resonance imaging (MRI) of inhaled hyperpolarized 129Xe gas as a non-invasive reference to which airflow velocities calculated via CFD can be compared. To that end, we performed subject-specific CFD simulations in airway models derived from 1H MRI, and using respiratory flowrate measurements acquired synchronously with MRI. Airflow velocity vectors calculated by CFD simulations were then qualitatively and quantitatively compared to velocity maps derived from PC velocimetry MRI of inhaled hyperpolarized 129Xe gas. The results show both techniques produce similar spatial distributions of high velocity regions in the anterior-posterior and foot-head directions, indicating good qualitative agreement. Statistically significant correlations and low Bland-Altman bias between the local velocity values produced by the two techniques indicates quantitative agreement. This preliminary in vivo comparison of respiratory airway CFD and PC MRI of hyperpolarized 129Xe gas demonstrates the feasibility of PC MRI as a technique to validate respiratory CFD and forms the basis for further comprehensive validation studies. This study is therefore a first step in the pathway towards clinical adoption of respiratory CFD.Computational fluid dynamics (CFD) simulations of respiratory airflow have the potential to change the clinical assessment of regional airway function in health and disease, in pulmonary medicine and otolaryngology. For example, in diseases where multiple sites of airway obstruction occur, such as obstructive sleep apnea (OSA), CFD simulations can identify which sites of obstruction contribute most to airway resistance and may therefore be candidate sites for airway surgery. The main barrier to clinical uptake of respiratory CFD to date has been the difficulty in validating CFD results against a clinical gold standard. Invasive instrumentation of the upper airway to measure respiratory airflow velocity or pressure can disrupt the airflow and alter the subject's natural breathing patterns. Therefore, in this study, we instead propose phase contrast (PC) velocimetry magnetic resonance imaging (MRI) of inhaled hyperpolarized 129Xe gas as a non-invasive reference to which airflow velocities calculated via CFD can be compared. To that end, we performed subject-specific CFD simulations in airway models derived from 1H MRI, and using respiratory flowrate measurements acquired synchronously with MRI. Airflow velocity vectors calculated by CFD simulations were then qualitatively and quantitatively compared to velocity maps derived from PC velocimetry MRI of inhaled hyperpolarized 129Xe gas. The results show both techniques produce similar spatial distributions of high velocity regions in the anterior-posterior and foot-head directions, indicating good qualitative agreement. Statistically significant correlations and low Bland-Altman bias between the local velocity values produced by the two techniques indicates quantitative agreement. This preliminary in vivo comparison of respiratory airway CFD and PC MRI of hyperpolarized 129Xe gas demonstrates the feasibility of PC MRI as a technique to validate respiratory CFD and forms the basis for further comprehensive validation studies. This study is therefore a first step in the pathway towards clinical adoption of respiratory CFD. Computational fluid dynamics (CFD) simulations of respiratory airflow have the potential to change the clinical assessment of regional airway function in health and disease, in pulmonary medicine and otolaryngology. For example, in diseases where multiple sites of airway obstruction occur, such as obstructive sleep apnea (OSA), CFD simulations can identify which sites of obstruction contribute most to airway resistance and may therefore be candidate sites for airway surgery. The main barrier to clinical uptake of respiratory CFD to date has been the difficulty in validating CFD results against a clinical gold standard. Invasive instrumentation of the upper airway to measure respiratory airflow velocity or pressure can disrupt the airflow and alter the subject's natural breathing patterns. Therefore, in this study, we instead propose phase contrast (PC) velocimetry magnetic resonance imaging (MRI) of inhaled hyperpolarized 129Xe gas as a non-invasive reference to which airflow velocities calculated via CFD can be compared. To that end, we performed subject-specific CFD simulations in airway models derived from 1H MRI, and using respiratory flowrate measurements acquired synchronously with MRI. Airflow velocity vectors calculated by CFD simulations were then qualitatively and quantitatively compared to velocity maps derived from PC velocimetry MRI of inhaled hyperpolarized 129Xe gas. The results show both techniques produce similar spatial distributions of high velocity regions in the anterior-posterior and foot-head directions, indicating good qualitative agreement. Statistically significant correlations and low Bland-Altman bias between the local velocity values produced by the two techniques indicates quantitative agreement. This preliminary in vivo comparison of respiratory airway CFD and PC MRI of hyperpolarized 129Xe gas demonstrates the feasibility of PC MRI as a technique to validate respiratory CFD and forms the basis for further comprehensive validation studies. This study is therefore a first step in the pathway towards clinical adoption of respiratory CFD. Computational fluid dynamics (CFD) simulations of respiratory airflow have the potential to change the clinical assessment of regional airway function in health and disease, in pulmonary medicine and otolaryngology. For example, in diseases where multiple sites of airway obstruction occur, such as obstructive sleep apnea (OSA), CFD simulations can identify which sites of obstruction contribute most to airway resistance and may therefore be candidate sites for airway surgery. The main barrier to clinical uptake of respiratory CFD to date has been the difficulty in validating CFD results against a clinical gold standard. Invasive instrumentation of the upper airway to measure respiratory airflow velocity or pressure can disrupt the airflow and alter the subject’s natural breathing patterns. Therefore, in this study, we instead propose phase contrast (PC) velocimetry magnetic resonance imaging (MRI) of inhaled hyperpolarized 129 Xe gas as a non-invasive reference to which airflow velocities calculated via CFD can be compared. To that end, we performed subject-specific CFD simulations in airway models derived from 1 H MRI, and using respiratory flowrate measurements acquired synchronously with MRI. Airflow velocity vectors calculated by CFD simulations were then qualitatively and quantitatively compared to velocity maps derived from PC velocimetry MRI of inhaled hyperpolarized 129 Xe gas. The results show both techniques produce similar spatial distributions of high velocity regions in the anterior-posterior and foot-head directions, indicating good qualitative agreement. Statistically significant correlations and low Bland-Altman bias between the local velocity values produced by the two techniques indicates quantitative agreement. This preliminary in vivo comparison of respiratory airway CFD and PC MRI of hyperpolarized 129 Xe gas demonstrates the feasibility of PC MRI as a technique to validate respiratory CFD and forms the basis for further comprehensive validation studies. This study is therefore a first step in the pathway towards clinical adoption of respiratory CFD. |
| Author | Amin, Raouf S. Xiao, Qiwei Woods, Jason C. Willmering, Matthew M. Wang, Hui Bates, Alister J. Thomen, Robert P. Stewart, Neil J. Gunatilaka, Chamindu C. Schuh, Andreas Krishnamoorthy, Guruprasad Dumoulin, Charles L. |
| AuthorAffiliation | 6 MR Clinical Science, Philips, Cincinnati, OH, United States of America 5 MR Clinical Science, Philips, Rochester, MN, United States of America 9 Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States of America 8 Department of Radiology, Cincinnati Children’s Hospital, Cincinnati, OH, United States of America 1 Division of Pulmonary Medicine, Center for Pulmonary Imaging Research, Cincinnati Children’s Hospital, Cincinnati, OH, United States of America 7 Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, OH, United States of America Technion Israel Institute of Technology, ISRAEL 4 Department of Computing, Imperial College London, London, United Kingdom 2 Department of Infection, Immunity & Cardiovascular Disease, POLARIS Group, Imaging Sciences, University of Sheffield, Sheffield, United Kingdom 3 Pulmonary Imaging Research Laboratory, University of Missouri School of Medicine, Columbia, Missouri, United States of |
| AuthorAffiliation_xml | – name: 6 MR Clinical Science, Philips, Cincinnati, OH, United States of America – name: 3 Pulmonary Imaging Research Laboratory, University of Missouri School of Medicine, Columbia, Missouri, United States of America – name: Technion Israel Institute of Technology, ISRAEL – name: 1 Division of Pulmonary Medicine, Center for Pulmonary Imaging Research, Cincinnati Children’s Hospital, Cincinnati, OH, United States of America – name: 2 Department of Infection, Immunity & Cardiovascular Disease, POLARIS Group, Imaging Sciences, University of Sheffield, Sheffield, United Kingdom – name: 7 Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, OH, United States of America – name: 9 Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States of America – name: 4 Department of Computing, Imperial College London, London, United Kingdom – name: 8 Department of Radiology, Cincinnati Children’s Hospital, Cincinnati, OH, United States of America – name: 5 MR Clinical Science, Philips, Rochester, MN, United States of America |
| Author_xml | – sequence: 1 givenname: Qiwei surname: Xiao fullname: Xiao, Qiwei – sequence: 2 givenname: Neil J. orcidid: 0000-0001-8358-394X surname: Stewart fullname: Stewart, Neil J. – sequence: 3 givenname: Matthew M. orcidid: 0000-0002-4356-9622 surname: Willmering fullname: Willmering, Matthew M. – sequence: 4 givenname: Chamindu C. orcidid: 0000-0003-2713-6533 surname: Gunatilaka fullname: Gunatilaka, Chamindu C. – sequence: 5 givenname: Robert P. surname: Thomen fullname: Thomen, Robert P. – sequence: 6 givenname: Andreas surname: Schuh fullname: Schuh, Andreas – sequence: 7 givenname: Guruprasad surname: Krishnamoorthy fullname: Krishnamoorthy, Guruprasad – sequence: 8 givenname: Hui surname: Wang fullname: Wang, Hui – sequence: 9 givenname: Raouf S. surname: Amin fullname: Amin, Raouf S. – sequence: 10 givenname: Charles L. surname: Dumoulin fullname: Dumoulin, Charles L. – sequence: 11 givenname: Jason C. surname: Woods fullname: Woods, Jason C. – sequence: 12 givenname: Alister J. surname: Bates fullname: Bates, Alister J. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34411195$$D View this record in MEDLINE/PubMed |
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| Copyright | 2021 Xiao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 Xiao et al 2021 Xiao et al |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: Drs Hui Wang and Guruprasad Krishnamoorthy are employees of Philips. Philips did not fund this study, nor did they have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript other than the roles of these authors as articulated in the ‘author contributions’ section. These conflicts do not alter our adherence to PLOS ONE policies on sharing data and materials. No other competing interests exist. |
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| SubjectTerms | Adult Aerodynamics Air flow Anatomy & physiology Apnea Biology and Life Sciences Boundary conditions Comorbidity Computational fluid dynamics Computer applications Computer Simulation Fluid dynamics Gases Hospitals Humans Hydrodynamics In vivo methods and tests Instrumentation Lung diseases Magnetic resonance Magnetic Resonance Imaging Male Mathematical models Medicine Medicine and Health Sciences Methods Nasopharynx - diagnostic imaging Otolaryngology Pediatrics Phase contrast Physical Sciences Physiology Pulmonary Ventilation Pulmonology Qualitative analysis Regions Research and Analysis Methods Respiratory tract Rheology Simulation Sleep apnea Sleep disorders Software Spatial distribution Statistical analysis Supervision Trachea - diagnostic imaging Velocimetry Velocity Velocity measurement Xenon 129 |
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| Title | Human upper-airway respiratory airflow: In vivo comparison of computational fluid dynamics simulations and hyperpolarized 129Xe phase contrast MRI velocimetry |
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