1
|
Galloy AE, Reinhardt JM, Raghavan ML. Role of lung lobar sliding on parenchymal distortion during breathing. J Appl Physiol (1985) 2023; 135:534-541. [PMID: 37439240 PMCID: PMC10538991 DOI: 10.1152/japplphysiol.00631.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 06/14/2023] [Accepted: 07/10/2023] [Indexed: 07/14/2023] Open
Abstract
Sliding between lung lobes along lobar fissures is a poorly understood aspect of lung mechanics. The objective of this study was to test the hypothesis that lobar sliding helps reduce distortion in the lung parenchyma during breathing. Finite element models of left lungs with geometries and boundary conditions derived from medical images of human subjects were developed. Effect of lobar sliding was studied by comparing nonlinear finite elastic contact mechanics simulations that allowed and disallowed lobar sliding. Lung parenchymal distortion during simulated breath-holds and tidal breathing was quantified with the model's spatial mean anisotropic deformation index (ADI), a measure of directional preference in volume change that varies spatially in the lung. Models that allowed lobar sliding had significantly lower mean ADI (i.e., lesser parenchymal distortion) than models that disallowed lobar sliding under simulations of both tidal breathing (5.3% median difference, P = 0.008, n = 8) and lung deformation between breath-holds at total lung capacity and functional residual capacity (3.2% median difference, P = 0.03, n = 6). This effect was most pronounced in the lower lobe where lobar sliding reduced parenchymal distortion with statistical significance, but not in the upper lobe. In addition, more lobar sliding was correlated with greater reduction in distortion between sliding and nonsliding models in our study cohorts (Pearson's correlation coefficient of 0.95 for tidal breathing, 0.87 for breath-holds, and 0.91 for the combined dataset). These findings are consistent with the hypothesis that lung lobar sliding reduces parenchymal distortion during breathing.NEW & NOTEWORTHY The role of lobar sliding in lung mechanics is poorly understood. Delineating this role could help explain how breathing is affected by anatomical differences between subjects such as incomplete and missing lobar fissures. We used computational contact mechanics models of lungs from human subjects to delineate the effect of lobar sliding by comparing simulations that allowed and disallowed sliding. We found evidence consistent with the hypothesis that lung lobar sliding reduces parenchymal distortion during breathing.
Collapse
Affiliation(s)
- Adam E Galloy
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States
| | - Joseph M Reinhardt
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States
| | - Madhavan L Raghavan
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States
| |
Collapse
|
2
|
Kumar H, Green R, Cornfeld DM, Condron P, Emsden T, Elsayed A, Zhao D, Gilbert K, Nash MP, Clark AR, Tawhai MH, Burrowes K, Murphy R, Tayebi M, McGeown J, Kwon E, Shim V, Wang A, Choisne J, Carman L, Besier T, Handsfield G, Babarenda Gamage TP, Shen J, Maso Talou G, Safaei S, Maller JJ, Taylor D, Potter L, Holdsworth SJ, Wilson GA. Roadmap for an imaging and modelling paediatric study in rural NZ. Front Physiol 2023; 14:1104838. [PMID: 36969588 PMCID: PMC10036853 DOI: 10.3389/fphys.2023.1104838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 01/30/2023] [Indexed: 03/12/2023] Open
Abstract
Our study methodology is motivated from three disparate needs: one, imaging studies have existed in silo and study organs but not across organ systems; two, there are gaps in our understanding of paediatric structure and function; three, lack of representative data in New Zealand. Our research aims to address these issues in part, through the combination of magnetic resonance imaging, advanced image processing algorithms and computational modelling. Our study demonstrated the need to take an organ-system approach and scan multiple organs on the same child. We have pilot tested an imaging protocol to be minimally disruptive to the children and demonstrated state-of-the-art image processing and personalized computational models using the imaging data. Our imaging protocol spans brain, lungs, heart, muscle, bones, abdominal and vascular systems. Our initial set of results demonstrated child-specific measurements on one dataset. This work is novel and interesting as we have run multiple computational physiology workflows to generate personalized computational models. Our proposed work is the first step towards achieving the integration of imaging and modelling improving our understanding of the human body in paediatric health and disease.
Collapse
Affiliation(s)
- Haribalan Kumar
- Mātai Medical Research Institute, Gisborne, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- GE Healthcare (Australia & New Zealand), Auckland, New Zealand
| | - Robby Green
- Mātai Medical Research Institute, Gisborne, New Zealand
| | - Daniel M. Cornfeld
- Mātai Medical Research Institute, Gisborne, New Zealand
- Faculty of Medical and Health Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Paul Condron
- Mātai Medical Research Institute, Gisborne, New Zealand
- Faculty of Medical and Health Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Taylor Emsden
- Mātai Medical Research Institute, Gisborne, New Zealand
- Faculty of Medical and Health Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Ayah Elsayed
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Auckland University of Technology, Auckland, New Zealand
| | - Debbie Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Kat Gilbert
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Martyn P. Nash
- Mātai Medical Research Institute, Gisborne, New Zealand
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Alys R. Clark
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn H. Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Kelly Burrowes
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Rinki Murphy
- Faculty of Medical and Health Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Maryam Tayebi
- Mātai Medical Research Institute, Gisborne, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Josh McGeown
- Mātai Medical Research Institute, Gisborne, New Zealand
| | - Eryn Kwon
- Mātai Medical Research Institute, Gisborne, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Faculty of Medical and Health Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Alan Wang
- Mātai Medical Research Institute, Gisborne, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Faculty of Medical and Health Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Julie Choisne
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Laura Carman
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Thor Besier
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Geoffrey Handsfield
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | | | - Jiantao Shen
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Gonzalo Maso Talou
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Soroush Safaei
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Jerome J. Maller
- GE Healthcare (Australia & New Zealand), Auckland, New Zealand
- Monash Alfred Psychiatry Research Centre, Melbourne, VIC, Australia
| | | | - Leigh Potter
- Mātai Medical Research Institute, Gisborne, New Zealand
| | - Samantha J. Holdsworth
- Mātai Medical Research Institute, Gisborne, New Zealand
- Faculty of Medical and Health Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand
- *Correspondence: Samantha J. Holdsworth,
| | | |
Collapse
|
3
|
Clark AR, Burrowes KS, Tawhai MH. Integrative Computational Models of Lung Structure-Function Interactions. Compr Physiol 2021; 11:1501-1530. [PMID: 33577123 DOI: 10.1002/cphy.c200011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Anatomically based integrative models of the lung and their interaction with other key components of the respiratory system provide unique capabilities for investigating both normal and abnormal lung function. There is substantial regional variability in both structure and function within the normal lung, yet it remains capable of relatively efficient gas exchange by providing close matching of air delivery (ventilation) and blood delivery (perfusion) to regions of gas exchange tissue from the scale of the whole organ to the smallest continuous gas exchange units. This is despite remarkably different mechanisms of air and blood delivery, different fluid properties, and unique scale-dependent anatomical structures through which the blood and air are transported. This inherent heterogeneity can be exacerbated in the presence of disease or when the body is under stress. Current computational power and data availability allow for the construction of sophisticated data-driven integrative models that can mimic respiratory system structure, function, and response to intervention. Computational models do not have the same technical and ethical issues that can limit experimental studies and biomedical imaging, and if they are solidly grounded in physiology and physics they facilitate investigation of the underlying interaction between mechanisms that determine respiratory function and dysfunction, and to estimate otherwise difficult-to-access measures. © 2021 American Physiological Society. Compr Physiol 11:1501-1530, 2021.
Collapse
Affiliation(s)
- Alys R Clark
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Kelly S Burrowes
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn H Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| |
Collapse
|
4
|
Lung and fissure shape is associated with age in healthy never-smoking adults aged 20-90 years. Sci Rep 2020; 10:16135. [PMID: 32999328 PMCID: PMC7528089 DOI: 10.1038/s41598-020-73117-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 09/10/2020] [Indexed: 11/08/2022] Open
Abstract
Lung shape could hold prognostic information for age-related diseases that affect lung tissue mechanics. We sought to quantify mean lung shape, its modes of variation, and shape associations with lung size, age, sex, and Body Mass Index (BMI) in healthy subjects across a seven-decade age span. Volumetric computed tomography from 83 subjects (49 M/34 F, BMI [Formula: see text]) was used to derive two statistical shape models using a principal component analysis. One model included, and the other controlled for, lung volume. Volume made the strongest contribution to shape when it was included. Shape had a strong relationship with age but not sex when volume was controlled for, and BMI had only a small but significant association with shape. The first principal shape mode was associated with decrease in the antero-posterior dimension from base to apex. In older subjects this was rapid and obvious, whereas younger subjects had relatively more constant dimension. A shift of the fissures of both lungs in the basal direction was apparent for the older subjects, consistent with a change in tissue elasticity with age. This study suggests a quantifiable structure-function relationship for the healthy adult lung that can potentially be exploited as a normative description against which abnormal can be compared.
Collapse
|