Quantitative Analysis of Lung Shape in Idiopathic Pulmonary Fibrosis: Insights Into Disease- and Age-Associated Patterns.

RATIONALE AND OBJECTIVES: Fibrotic scarring in idiopathic pulmonary fibrosis (IPF) typically develops first in the posterior-basal lung tissue before advancing to involve more of the lung. The complexity of lung shape in the costo-diaphragmatic region has been proposed as a potential factor in this regional development. Intrinsic and disease-related shape could therefore be important for understanding IPF risk and its staging. We hypothesized that lung and lobe shape in IPF would have important differences from controls. MATERIALS AND METHODS: A principal component (PC) analysis was used to derive a statistical shape model (SSM) of the lung for a control cohort aged > 50 years (N = 39), using segmented lung and fissure surface data from CT imaging. Individual patient shape models derived for baseline (N = 18) and follow-up (N = 16) CT scans in patients with IPF were projected to the SSM to describe shape as the sum of the SSM average and weighted PC modes. Associations between the first four PC shape modes, lung function, percentage of fibrosis (fibrosis%) and pulmonary vessel-related structures (PVRS%), and other tissue metrics were assessed and compared between the two cohorts. RESULTS: Shape was different between IPF and controls (P < 0.05 for all shape modes), with IPF shape forming a distinct shape cluster. Shape had a negative relationship with age in controls (P = 0.013), but a positive relationship with age in IPF (P = 0.026). Some features of shape changed on follow-up. Shape in IPF was associated with fibrosis% (P < 0.05) and PVRS% (P < 0.05). CONCLUSION: Quantitative comparison of lung and lobe shape in IPF with controls of a similar age reveals shape differences that are strongly associated with age and percent fibrosis. The clustering of IPF cohort shape suggests that it could be an important feature to describe disease.

A Wearable Open-Source electrical impedance tomography device.

Electrical impedance tomography (EIT) is medical imaging technique in which small electrical signals are used to map the electrical impedance distribution within the body. It is safe and non-invasive, which make it attractive for use in continuous monitoring or outpatient applications, but the high cost of commercial devices is an impediment to its adoption. Over the last 10 years, many research groups have developed their own EIT devices, but few designs for open-source EIT hardware are available. In this work, we present a complete open-source EIT system that is designed to be suitable for monitoring the lungs of free breathing subjects. The device is low-cost, wearable, and is designed to comply with the industry accepted safety standard for EIT. The device has been tested in two regimes: Firstly in terms of measurement uncertainty as a voltage measurement system, and secondly against a set of measures that have been proposed specifically for EIT hardware. The voltage measurement uncertainty of the device was measured to be - 0.7 % ± 0.36 mV. The EIT specific performance was measured in a phantom test designed to be as physiologically representative as practicable, and the device performed similarly to other published devices. This work will contribute to increased accessibility of EIT for study and will contribute to consensus on testing methodology for EIT devices.

A novel two-dimensional phantom for electrical impedance tomography using 3D printing.

Electrical impedance tomography (EIT) is an imaging method that can be used to image electrical impedance contrasts within various tissues of the body. To support development of EIT measurement systems, a phantom is required that represents the electrical characteristics of the imaging domain. No existing type of EIT phantom combines good performance in all three characteristics of resistivity resolution, spatial resolution, and stability. Here, a novel EIT phantom concept is proposed that uses 3D printed conductive material. Resistivity is controlled using the 3D printing infill percentage parameter, allowing arbitrary resistivity contrasts within the domain to be manufactured automatically. The concept of controlling resistivity through infill percentage is validated, and the manufacturing accuracy is quantified. A method for making electrical connections to the 3D printed material is developed. Finally, a prototype phantom is printed, and a sample EIT analysis is performed. The resulting phantom, printed with an Ultimaker S3, has high reported spatial resolution of 6.9 µm, 6.9 µm, and 2.5 µm for X, Y, and Z axis directions, respectively (X and Y being the horizontal axes, and Z the vertical). The number of resistivity levels that are manufacturable by varying infill percentage is 15 (calculated by dividing the available range of resistivities by two times the standard deviation of the manufacturing accuracy). This phantom construction technique will allow assessment of the performance of EIT devices under realistic physiological scenarios.

Pulmonary response prediction through personalized basis functions in a virtual patient model.

BACKGROUND AND OBJECTIVE: Recruitment maneuvers with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveoli collapse. However, determining a safe, effective, and patient-specific PEEP is not standardized, and this more optimal PEEP level evolves with patient condition, requiring personalised monitoring and care approaches to maintain optimal ventilation settings. METHODS: This research examines 3 physiologically relevant basis function sets (exponential, parabolic, cumulative) to enable better prediction of elastance evolution for a virtual patient or digital twin model of MV lung mechanics, including novel elements to model and predict distension elastance. Prediction accuracy and robustness are validated against recruitment maneuver data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0 to 12 cmH2O) and 14 pressure-controlled ventilation (PCV) patients at 4 different baseline PEEP levels (6 to 12 cmH2O), yielding 623 and 294 prediction cases, respectively. Predictions were made up to 12 cmH2O of added PEEP ahead, covering 6 × 2 cmH2O PEEP steps. RESULTS: The 3 basis function sets yield median absolute peak inspiratory pressure (PIP) prediction error of 1.63 cmH2O for VCV patients, and median peak inspiratory volume (PIV) prediction error of 0.028 L for PCV patients. The exponential basis function set yields a better trade-off of overall performance across VCV and PCV prediction than parabolic and cumulative basis function sets from other studies. Comparing predicted and clinically measured distension prediction in VCV demonstrated consistent, robust high accuracy with R2 = 0.90-0.95. CONCLUSIONS: The results demonstrate recruitment mechanics are best captured by an exponential basis function across different mechanical ventilation modes, matching physiological expectations, and accurately capture, for the first time, distension mechanics to within 5-10 % accuracy. Enabling the risk of lung injury to be predicted before changing ventilator settings. The overall outcomes significantly extend and more fully validate this digital twin or virtual mechanical ventilation patient model.

Evaluating Tissue Heterogeneity in the Radiologically Normal-Appearing Tissue in IPF Compared to Healthy Controls.

RATIONALE AND OBJECTIVES: Idiopathic Pulmonary Fibrosis (IPF) is a progressive interstitial lung disease characterised by heterogeneously distributed fibrotic lesions. The inter- and intra-patient heterogeneity of the disease has meant that useful biomarkers of severity and progression have been elusive. Previous quantitative computed tomography (CT) based studies have focussed on characterising the pathological tissue. However, we hypothesised that the remaining lung tissue, which appears radiologically normal, may show important differences from controls in tissue characteristics. MATERIALS AND METHODS: Quantitative metrics were derived from CT scans in IPF patients (N = 20) and healthy controls with a similar age (N = 59). An automated quantitative software (CALIPER, Computer-Aided Lung Informatics for Pathology Evaluation and Rating) was used to classify tissue as normal-appearing, fibrosis, or low attenuation area. Densitometry metrics were calculated for all lung tissue and for only the normal-appearing tissue. Heterogeneity of lung tissue density was quantified as coefficient of variation and by quadtree. Associations between measured lung function and quantitative metrics were assessed and compared between the two cohorts. RESULTS: All metrics were significantly different between controls and IPF (p < 0.05), including when only the normal tissue was evaluated (p < 0.04). Density in the normal tissue was 14% higher in the IPF participants than controls (p < 0.001). The normal-appearing tissue in IPF had heterogeneity metrics that exhibited significant positive relationships with the percent predicted diffusion capacity for carbon monoxide. CONCLUSION: We provide quantitative assessment of IPF lung tissue characteristics compared to a healthy control group of similar age. Tissue that appears visually normal in IPF exhibits subtle but quantifiable differences that are associated with lung function and gas exchange.

Sustained vs. Intratidal Recruitment in the Injured Lung During Airway Pressure Release Ventilation: A Computational Modeling Perspective.

INTRODUCTION: During mechanical ventilation, cyclic recruitment and derecruitment (R/D) of alveoli result in focal points of heterogeneous stress throughout the lung. In the acutely injured lung, the rates at which alveoli can be recruited or derecruited may also be altered, requiring longer times at higher pressure levels to be recruited during inspiration, but shorter times at lower pressure levels to minimize collapse during exhalation. In this study, we used a computational model to simulate the effects of airway pressure release ventilation (APRV) on acinar recruitment, with varying inspiratory pressure levels and durations of exhalation. MATERIALS AND METHODS: The computational model consisted of a ventilator pressure source, a distensible breathing circuit, an endotracheal tube, and a porcine lung consisting of recruited and derecruited zones, as well as a transitional zone capable of intratidal R/D. Lung injury was simulated by modifying each acinus with an inflation-dependent surface tension. APRV was simulated for an inhalation duration (Thigh) of 4.0 seconds, inspiratory pressures (Phigh) of 28 and 40 cmH2O, and exhalation durations (Tlow) ranging from 0.2 to 1.5 seconds. RESULTS: Both sustained acinar recruitment and intratidal R/D within the subtree were consistently higher for Phigh of 40 cmH2O vs. 28 cmH2O, regardless of Tlow. Increasing Tlow was associated with decreasing sustained acinar recruitment, but increasing intratidal R/D, within the subtree. Increasing Tlow was associated with decreasing elastance of both the total respiratory system and transitional subtree of the model. CONCLUSIONS: Our computational model demonstrates the confounding effects of cyclic R/D, sustained recruitment, and parenchymal strain stiffening on estimates of total lung elastance during APRV. Increasing inspiratory pressures leads to not only more sustained recruitment of unstable acini but also more intratidal R/D. Our model indicates that higher inspiratory pressures should be used in conjunction with shorter exhalation times, to avoid increasing intratidal R/D.

Improved Electrical Impedance Tomography Reconstruction via a Bayesian Approach With an Anatomical Statistical Shape Model.

OBJECTIVE: electrical impedance tomography (EIT) is a promising technique for rapid and continuous bedside monitoring of lung function. Accurate and reliable EIT reconstruction of ventilation requires patient-specific shape information. However, this shape information is often not available and current EIT reconstruction methods typically have limited spatial fidelity. This study sought to develop a statistical shape model (SSM) of the torso and lungs and evaluate whether patient-specific predictions of torso and lung shape could enhance EIT reconstructions in a Bayesian framework. METHODS: torso and lung finite element surface meshes were fitted to computed tomography data from 81 participants, and a SSM was generated using principal component analysis and regression analyses. Predicted shapes were implemented in a Bayesian EIT framework and were quantitatively compared to generic reconstruction methods. RESULTS: Five principal shape modes explained 38% of the cohort variance in lung and torso geometry, and regression analysis yielded nine total anthropometrics and pulmonary function metrics that significantly predicted these shape modes. Incorporation of SSM-derived structural information enhanced the accuracy and reliability of the EIT reconstruction as compared to generic reconstructions, demonstrated by reduced relative error, total variation, and Mahalanobis distance. CONCLUSION: As compared to deterministic approaches, Bayesian EIT afforded more reliable quantitative and visual interpretation of the reconstructed ventilation distribution. However, no conclusive improvement of reconstruction performance using patient specific structural information was observed as compared to the mean shape of the SSM. SIGNIFICANCE: The presented Bayesian framework builds towards a more accurate and reliable method for ventilation monitoring via EIT.

Roadmap for an imaging and modelling paediatric study in rural NZ.

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.

Pulmonary vessel volume in idiopathic pulmonary fibrosis compared with healthy controls aged > 50 years.

Idiopathic pulmonary fibrosis (IPF) is characterised by progressive fibrosing interstitial pneumonia with an associated irreversible decline in lung function and quality of life. IPF prevalence increases with age, appearing most frequently in patients aged > 50 years. Pulmonary vessel-like volume (PVV) has been found to be an independent predictor of mortality in IPF and other interstitial lung diseases, however its estimation can be impacted by artefacts associated with image segmentation methods and can be confounded by adjacent fibrosis. This study compares PVV in IPF patients (N = 21) with PVV from a healthy cohort aged > 50 years (N = 59). The analysis includes a connected graph-based approach that aims to minimise artefacts contributing to calculation of PVV. We show that despite a relatively low extent of fibrosis in the IPF cohort (20% of the lung volume), PVV is 2-3 times higher than in controls. This suggests that a standardised method to calculate PVV that accounts for tree connectivity could provide a promising tool to provide early diagnostic or prognostic information in IPF patients and other interstitial lung disease.

Non-invasive over-distension measurements: data driven vs model-based.

Clinical measurements offer bedside monitoring aiming to minimise unintended over-distension, but have limitations and cannot be predicted for changes in mechanical ventilation (MV) settings and are only available in certain MV modes. This study introduces a non-invasive, real-time over-distension measurement, which is robust, predictable, and more intuitive than current methods. The proposed over-distension measurement, denoted as OD, is compared with the clinically proven stress index (SI). Correlation is analysed via R2 and Spearman rs. The OD safe range corresponding to the unit-less SI safe range (0.95-1.05) is calibrated by sensitivity and specificity test. Validation is fulfilled with 19 acute respiratory distress syndrome (ARDS) patients data (196 cases), including assessment across ARDS severity. Overall correlation between OD and SI yielded R2 = 0.76 and Spearman rs = 0.89. Correlation is higher considering only moderate and severe ARDS patients. Calibration of OD to SI yields a safe range defined: 0 ≤ OD ≤ 0.8 cmH2O. The proposed OD offers an efficient, general, real-time measurement of patient-specific lung mechanics, which is more intuitive and robust than SI. OD eliminates the limitations of SI in MV mode and its less intuitive lung status value. Finally, OD can be accurately predicted for new ventilator settings via its foundation in a validated predictive personalized lung mechanics model. Therefore, OD offers potential clinical value over current clinical methods.

Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model.

BACKGROUND: Patient-specific lung mechanics during mechanical ventilation (MV) can be identified from measured waveforms of fully ventilated, sedated patients. However, asynchrony due to spontaneous breathing (SB) effort can be common, altering these waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics. METHODS: Changes in patient-specific lung elastance over a pressure-volume (PV) loop, identified using hysteresis loop analysis (HLA), are used to detect the occurrence of asynchrony and identify its type and pattern. The identified HLA parameters are then combined with a nonlinear mechanics hysteresis loop model (HLM) to extract and reconstruct ventilated waveforms unaffected by asynchronous breaths. Asynchrony magnitude can then be quantified using an energy-dissipation metric, Easyn, comparing PV loop area between model-reconstructed and original, altered asynchronous breathing cycles. Performance is evaluated using both test-lung experimental data with a known ground truth and clinical data from four patients with varying levels of asynchrony. RESULTS: Root mean square errors for reconstructed PV loops are within 5% for test-lung experimental data, and 10% for over 90% of clinical data. Easyn clearly matches known asynchrony magnitude for experimental data with RMS errors < 4.1%. Clinical data performance shows 57% breaths having Easyn > 50% for Patient 1 and 13% for Patient 2. Patient 3 only presents 20% breaths with Easyn > 10%. Patient 4 has Easyn = 0 for 96% breaths showing accuracy in a case without asynchrony. CONCLUSIONS: Experimental test-lung validation demonstrates the method's reconstruction accuracy and generality in controlled scenarios. Clinical validation matches direct observations of asynchrony in incidence and quantifies magnitude, including cases without asynchrony, validating its robustness and potential efficacy as a clinical real-time asynchrony monitoring tool.

Simulating Multi-Scale Pulmonary Vascular Function by Coupling Computational Fluid Dynamics With an Anatomic Network Model.

The function of the pulmonary circulation is truly multi-scale, with blood transported through vessels from centimeter to micron scale. There are scale-dependent mechanisms that govern the flow in the pulmonary vascular system. However, very few computational models of pulmonary hemodynamics capture the physics of pulmonary perfusion across the spatial scales of functional importance in the lung. Here we present a multi-scale model that incorporates the 3-dimensional (3D) complexities of pulmonary blood flow in the major vessels, coupled to an anatomically-based vascular network model incorporating the multiple contributing factors to capillary perfusion, including gravity. Using the model we demonstrate how we can predict the impact of vascular remodeling and occlusion on both macro-scale functional drivers (flow distribution between lungs, and wall shear stress) and micro-scale contributors to gas exchange. The model predicts interactions between 3D and 1D models that lead to a redistribution of blood between postures, both on a macro- and a micro-scale. This allows us to estimate the effect of posture on left and right pulmonary artery wall shear stress, with predictions varying by 0.75-1.35 dyne/cm2 between postures.

Over-distension prediction via hysteresis loop analysis and patient-specific basis functions in a virtual patient model.

BACKGROUND AND OBJECTIVE: Recruitment maneuvers (RMs) with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveolar collapse. However, a suboptimal PEEP could induce undesired injury in lungs by insufficient or excessive breath support. Thus, a predictive model for patient response under PEEP changes could improve clinical care and lower risks. METHODS: This research adds novel elements to a virtual patient model to identify and predict patient-specific lung distension to optimise and personalise care. Model validity and accuracy are validated using data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0-12cmH2O), yielding 623 prediction cases. Predictions were made up to ΔPEEP = 12cmH2O ahead covering 6x2cmH2O PEEP steps. RESULTS: Using the proposed lung distension model, 90% of absolute peak inspiratory pressure (PIP) prediction errors compared to clinical measurement are within 3.95cmH2O, compared with 4.76cmH2O without this distension term. Comparing model-predicted and clinically measured distension had high correlation increasing to R2 = 0.93-0.95 if maximum ΔPEEP ≤ 6cmH2O. Predicted dynamic functional residual capacity (Vfrc) changes as PEEP rises yield 0.013L median prediction error for both prediction groups and overall R2 of 0.84. CONCLUSIONS: Overall results demonstrate nonlinear distension mechanics are accurately captured in virtual lung mechanics patients for mechanical ventilation, for the first time. This result can minimise the risk of lung injury by predicting its potential occurrence of distension before changing ventilator settings. The overall outcomes significantly extend and more fully validate this virtual mechanical ventilation patient model.

A computational model of contributors to pulmonary hypertensive disease: impacts of whole lung and focal disease distributions.

Pulmonary hypertension has multiple etiologies and so can be difficult to diagnose, prognose, and treat. Diagnosis is typically made via invasive hemodynamic measurements in the main pulmonary artery and is based on observed elevation of mean pulmonary artery pressure. This static mean pressure enables diagnosis, but does not easily allow assessment of the severity of pulmonary hypertension, nor the etiology of the disease, which may impact treatment. Assessment of the dynamic properties of pressure and flow data obtained from catheterization potentially allows more meaningful assessment of the strain on the right heart and may help to distinguish between disease phenotypes. However, mechanistic understanding of how the distribution of disease in the lung leading to pulmonary hypertension impacts the dynamics of blood flow in the main pulmonary artery and/or the pulmonary capillaries is lacking. We present a computational model of the pulmonary vasculature, parameterized to characteristic features of pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension to help understand how the two conditions differ in terms of pulmonary vascular response to disease. Our model incorporates key features known to contribute to pulmonary vascular function in health and disease, including anatomical structure and multiple contributions from gravity. The model suggests that dynamic measurements obtained from catheterization potentially distinguish between distal and proximal vasculopathy typical of pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension. However, the model suggests a non-linear relationship between these data and vascular structural changes typical of pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension which may impede analysis of these metrics to distinguish between cohorts.

Integrative Computational Models of Lung Structure-Function Interactions.

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.

Lung and fissure shape is associated with age in healthy never-smoking adults aged 20-90 years.

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.

Prediction of lung mechanics throughout recruitment maneuvers in pressure-controlled ventilation.

Mechanical ventilation (MV) is a core therapy in the intensive care unit (ICU). Some patients rely on MV to support breathing. However, it is a difficult therapy to optimise, where inter- and intra- patient variability leads to significantly increased risk of lung damage. Excessive volume and/or pressure can cause volutrauma or barotrauma, resulting in increased length of time on ventilation, length of stay, cost and mortality. Virtual patient modelling has changed care in other areas of ICU medicine, enabling more personalized and optimal care, and have emerged for volume-controlled MV. This research extends this MV virtual patient model into the increasingly more commonly used pressure-controlled MV mode. The simulation methods are extended to use pressure, instead of both volume and flow, as the known input, increasing the output variables to be predicted (flow and its integral, volume). The model and methods are validated using data from N = 14 pressure-control ventilated patients during recruitment maneuvers, with n = 558 prediction tests over changes of PEEP ranging from 2 to 16 cmH2O. Prediction errors for peak inspiratory volume for an increase of 16 cmH2O were 80 [30 - 140] mL (15.9 [8.4 - 31.0]%), with RMS fitting errors of 0.05 [0.03 - 0.12] L. These results show very good prediction accuracy able to guide personalised MV care.

High-Frequency Oscillatory Ventilation and Ventilator-Induced Lung Injury: Size Does Matter.

OBJECTIVES: The theoretical basis for minimizing tidal volume during high-frequency oscillatory ventilation may not be appropriate when lung tissue stretch occurs heterogeneously and/or rapidly. The objective of this study was to assess the extent to which increased ventilation heterogeneity may contribute to ventilator-induced lung injury during high-frequency oscillatory ventilation in adults compared with neonates on the basis of lung size, using a computational model of human lungs. DESIGN: Computational modeling study. SETTING: Research laboratory. SUBJECTS: High-fidelity, 3D computational models of human lungs, scaled to various sizes representative of neonates, children, and adults, with varying injury severity. All models were generated from one thoracic CT image of a healthy adult male. INTERVENTIONS: Oscillatory ventilation was simulated in each lung model at frequencies ranging from 0.2 to 40 Hz. Sinusoidal flow oscillations were delivered at the airway opening of each model and distributed through the lungs according to regional parenchymal mechanics. MEASUREMENTS AND MAIN RESULTS: Acinar flow heterogeneity was assessed by the coefficient of variation in flow magnitudes across all acini in each model. High-frequency oscillatory ventilation simulations demonstrated increasing heterogeneity of regional parenchymal flow with increasing lung size, with decreasing ratio of deadspace to total acinar volume, and with increasing frequency above lung corner frequency and resonant frequency. Potential for resonant amplification was greatest in injured adult-sized lungs with higher regional quality factors indicating the presence of underdamped lung regions. CONCLUSIONS: The potential for ventilator-induced lung injury during high-frequency oscillatory ventilation is enhanced at frequencies above lung corner frequency or resonant frequency despite reduced tidal volumes, especially in adults, due to regional amplification of heterogeneous flow. Measurements of corner frequency and resonant frequency should be considered during high-frequency oscillatory ventilation management.

Ventilation/Perfusion Matching: Of Myths, Mice, and Men.

Despite a huge range in lung size between species, there is little measured difference in the ability of the lung to provide a well-matched air flow (ventilation) to blood flow (perfusion) at the gas exchange tissue. Here, we consider the remarkable similarities in ventilation/perfusion matching between species through a biophysical lens and consider evidence that matching in large animals is dominated by gravity but in small animals by structure.

Strain, strain rate, and mechanical power: An optimization comparison for oscillatory ventilation.

The purpose of this study was to assess the potential for optimization of mechanical ventilator waveforms using multiple frequencies of oscillatory flow delivered simultaneously to minimize the risk of ventilator-induced lung injury (VILI) associated with regional strain, strain rate, and mechanical power. Optimization was performed using simulations of distributed oscillatory flow and gas transport in a computational model of anatomically derived branching airway segments and viscoelastic terminal acini under healthy and injured conditions. Objective functions defined by regional strain or strain rate were minimized by single-frequency ventilation waveforms using the highest or lowest frequencies available, respectively. However, a mechanical power objective function was minimized by a combination of multiple frequencies delivered simultaneously. This simulation study thus demonstrates the potential for multifrequency oscillatory ventilation to reduce regional mechanical power in comparison to single-frequency ventilation, and thereby reduce the risk of VILI.