Model Of Lung Mechanics Research Articles

A switching lung mechanics model for detection of expiratory flow limitation

Abstract Expiratory flow limitation (EFL) is an often unrecognized clinical condition with a multitude of negative implications. A mathematical EFL model is proposed to detect flow limitations automatically. The EFL model is a switching one-compartment lung mechanics model with a volume-dependent airway resistance to simulate the dynamic behavior during expiration. The EFL detection is based on a breath-by-breath model parameter identification and validated on clinical data of mechanically ventilated patients. In the severe flow limitation group 93.9 % ± 5 % and in the no limitation group 10.2 % ± 13.7 % of the breaths are detected as EFL. Based on the high detection rate of EFL, these results support the usefulness of the EFL detection. It is a first step toward an automated detection of EFL in clinical applications and may help to reduce underdiagnosis of EFL.

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

Background and ObjectiveRecruitment 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. MethodsThis 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. ResultsThe 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. ConclusionsThe 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.

An in silico approach to understanding the interaction between cardiovascular and pulmonary lymphatic dysfunction.

The lung is extremely sensitive to interstitial fluid balance, yet the role of pulmonary lymphatics in lung fluid homeostasis and its interaction with cardiovascular pressures is poorly understood. In health, there is a fine balance between fluid extravasated from the pulmonary capillaries into the interstitium and the return of fluid to the circulation via the lymphatic vessels. This balance is maintained by an extremely interdependent system governed by pressures in the fluids (air and blood) and tissue (interstitium), lung motion during breathing, and the permeability of the tissues. Chronic elevation in left atrial pressure (LAP) due to left heart disease increases the capillary blood pressure. The consequent fluid accumulation in the delicate lung tissue increases its weight, decreases its compliance, and impairs gas exchange. This interdependent system is difficult, if not impossible, to study experimentally. Computational modeling provides a unique perspective to analyze fluid movement in the cardiopulmonary vasculature in health and disease. We have developed an initial in silico model of pulmonary lymphatic function using an anatomically structured model to represent ventilation and perfusion and underlying biophysical laws governing fluid transfer at the interstitium. This novel model was tested against increased LAP and noncardiogenic effects (increased permeability). The model returned physiologically reasonable values for all applications, predicting pulmonary edema when LAP reached 25 mmHg and with increased permeability.NEW & NOTEWORTHY This model presents a novel approach to understanding the interaction between cardiac dysfunction and pulmonary lymphatic function, using anatomically structured models and biophysical equations to estimate regional variation in fluid transport from blood to interstitial and lymphatic flux. This fluid transport model brings together advanced models of ventilation, perfusion, and lung mechanics to produce a detailed model of fluid transport in health and various altered pathological conditions.

Lung de-recruitment in the allergic asthma of obesity: evidence from an anatomically based inverse model.

The increase in asthma associated with the obesity epidemic cannot simply be due to airway hyperresponsiveness from chronic lung compression because chronic lung compression is a feature of obesity in general. We therefore sought to investigate what other factors might be at play in the impaired lung function seen in obese individuals with asthma. We measured respiratory system impedance in four groups-Lean Control, Lean Allergic Asthma, Obese Control, and Obese Allergic Asthma-before and after administration of albuterol. Impedance measurements were fit with an anatomically based computational model of lung mechanics that represents the airway tree as a branching structure with a uniform degree of asymmetry and a fixed radius scaling ratio, γ, between branches of sequential order. The two model parameters that define the airway tree, γ and tracheal radius, varied only modestly between the four study groups, indicating relatively minor differences in airway caliber. In contrast, respiratory system elastance was 57, 34, 143, and 271 cmH2O/L, respectively, for the four groups, suggesting that obesity induced significant lung de-recruitment that was exacerbated by allergic asthma. In addition, when the radii of the individual branches of the airway tree were varied randomly, we found that roughly half the terminal airways had to be closed to have the model fit the data well. We conclude that de-recruitment of small airways is a particular feature of Obese Allergic Asthma, and this can be inferred from respiratory system impedance fit with an anatomically based computational model.NEW & NOTEWORTHY Using a novel anatomically based computational model to interpret oscillometry measurements of impedance, we show that respiratory system elastance is increased in obesity and is increased dramatically in individuals with obese allergic asthma. A significant component of this increased elastance in obese allergic asthma appears to be due to closure of small airways rather than alveolar atelectasis, and this closure is partially mitigated by albuterol. These findings potentially point to nonpharmacological therapies in obese allergic asthma aimed at recruiting closed airways.

An identifiable model of lung mechanics to diagnose and monitor COPD

Background:Current methods to diagnose and monitor COPD employ spirometry as the gold standard to identify lung function reduction with reduced forced expiratory volume (FEV1)/vital capacity (VC) ratio. Current methods utilise linear assumptions regarding airway resistance, where nonlinear resistance modelling may provide rapid insight into patient specific condition and disease progression. This study examines model-based expiratory resistance in healthy lungs and those with progressively more severe COPD. Methods:Healthy and COPD pressure (P)[cmH2O] and flow (Q)[L/s] data is obtained from the literature, and 5 intermediate levels of COPD and responses are created to simulate COPD progression and assess model-based metric resolution. Linear and nonlinear single compartment models are used to identify changes in inspiratory (R1,insp) and linear (R1,exp)/nonlinear (R2Φ) expiratory resistance with disease severity and over the course of expiration. Results:R1,insp increases from 2.1 to 7.3 cmH2O/L/s, R1,exp increases from 2.4 to 10.0 cmH2O/L/s with COPD severity. Nonlinear R2Φ increases (mean R2Φ: 2.5 cmH2O/L/s (healthy) to 24.4 cmH2O/L/s (COPD)), with increasing end-expiratory nonlinearity as COPD severity increases. Conclusion:Expiratory resistance is increasingly highly nonlinear with COPD severity. These results show a simple, nonlinear model can capture fundamental COPD dynamics and progression from regular breathing data, and such an approach may be useful for patient-specific diagnosis and monitoring.

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.

Prediction and estimation of pulmonary response and elastance evolution for volume-controlled and pressure-controlled ventilation

Mechanical ventilation (MV) is a core treatment for patients suffering from respiratory disease and failure. However, MV settings are not standardized due to significant inter- and intra- patient variability in response to care, leading to variability in outcome. There is thus a need to personalize MV settings. This research significantly extends a single compartment lung mechanics model with physiologically relevant basis functions, and uses it to identify patient-specific lung mechanics and predict response to changes in MV settings. Nonlinear evolution of pulmonary elastance over positive end expiratory pressure (PEEP) is modelled by a newly proposed, physiologically relevant and simplified compensatory function to enable prediction of pulmonary response for both volume-controlled ventilation (VCV) and pressure-controlled ventilation (PCV), and identified as patient-specific using each patient’s data at a baseline PEEP. Predictions at higher PEEP levels test the validity of the proposed models based on errors in predicted peak inspiratory pressure (PIP) in two VCV trials and volume (PIV) in one PCV trial. A total of 210 prediction cases over 36 patients (22 VCV; 14 PCV) yielded absolute predicted PIP errors within 1.0 cmH2O (2.3%) and 3.3 cmH2O (7.3%) for 90% cases in VCV, while predicted PIV errors are within 0.073 L (16.8%) for 85% cases in PCV. In conclusion, a novel deterministic virtual patient model is presented, able to offer accurate prediction of pulmonary response across a wide range of PEEP changes for the two main MV modes used clinically, enabling predictive decision support in real-time to safely personalize and optimize care.

Minimal Lung Mechanics Basis-functions for a Mechanical Ventilation Virtual Patient

Abstract Mechanical ventilation (MV) is used in the intensive care unit (ICU) to treat patients with respiratory failure. However, MV settings are not standardized due to significant inter- and intra- patient variability in response to care, leading to variability in care, outcome, and cost. There is thus a need to personalize MV. This research extends a single compartment lung mechanics model with physiologically relevant basis functions, to identify patient-specific lung mechanics and predict response to changes in MV care. The nonlinear evolution of pulmonary elastance as positive-end-expiratory pressure (PEEP) changes is captured by a physiologically relevant, simplified compensatory equation as a function of PEEP and pressure identification error at the baseline PEEP level. It allows both patient-specific and general prediction of lung elastance of higher PEEP. The prediction outcome is validated with data from two volume-controlled ventilation (VCV) trials and one pressure-controlled ventilation (PCV) trial, where the biggest PEEP prediction interval is a clinically unrealistic 20cmH2O, comprising 210 prediction cases over 36 patients (22 VCV; 14 PCV). Predicted absolute peak inspiratory pressure (PIP) errors are within 1.0cmH2O and 3.3cmH2O for 90% cases in the two VCV trials, while predicted peak inspiratory tidal volume (PIV) errors are within 0.073L for 85% cases in studied PCV trial. The model presented provides a highly accurate, predictive virtual patient model across multiple MV modes and delivery methods, and over clinically unrealistically large changes. Low computational cost, and fast, easy parameterization enable model-based, predictive decision support in real-time to safely personalize and optimize MV care.

Alveolar Tissue Fiber and Surfactant Effects on Lung Mechanics-Model Development and Validation on ARDS and IPF Patients.

Goal: Alveolar compliance is a main determinant of lung airflow. The compliance of the alveoli is a function of their tissue fiber elasticity, fiber volume, and surface tension. The compliance varies during respiration because of the nonlinear nature of fiber elasticity and the time-varying surface tension coating the alveoli. Respiratory conditions, like acute respiratory distress syndrome (ARDS) and idiopathic pulmonary fibrosis (IPF) affect fiber elasticity, fiber volume and surface tension. In this paper, we study the alveolar tissue fibers and surface tension effects on lung mechanics. Methods: To better understand the lungs, we developed a physiology-based mathematical model to 1) describe the effect of tissue fiber elasticity, fiber volume and surface tension on alveolar compliance, and 2) the effect of time-varying alveolar compliance on lung mechanics for healthy, ARDS and IPF conditions. Results: We first present the model sensitivity analysis to show the effects of model parameters on the lung mechanics variables. Then, we perform model simulation and validate on healthy non-ventilated subjects and ventilated ARDS or IPF patients. Finally, we assess the robustness and stability of this dynamic system. Conclusions: We developed a mathematical model of the lung mechanics comprising alveolar tissue and surfactant properties that generates reasonable lung pressures and volumes compared to healthy, ARDS, and IPF patient data.

Parameter updating of a patient-specific lung mechanics model for optimising mechanical ventilation

Mechanical ventilation (MV) is the primary way to treat patients with acute respiratory distress syndrome (ARDS) in the intensive care unit (ICU). Positive-end-expiratory-pressure (PEEP) is often applied in MV to maximise gas exchange and prevent further lung damage. However, optimal MV with patient-specific PEEP level is subjective, based on clinician experience, thus increasing the risk variability, and cost of care. In this study, a single compartment lung mechanics model is employed to predict pressure outcomes at higher PEEP levels using data from lower PEEP levels. Particularly, a hysteresis loop analysis (HLA) algorithm is applied to both the dynostatic curve and pressure-volume (PV) loop to identify the lung elastance at lower PEEP levels. Pulmonary resistance can then be calculated with measured airway pressure and identified elastance. Finally, the elastance and resistance at higher PEEP levels are predicted based on model fitting techniques to obtain the pressure with changes of PEEP. The result of pressure fitting and prediction show 95% of the airway pressure curve errors are within 3% across all the 8 data sets, while peak-inspiratory-pressure (PIP) prediction errors are within 1% for all data sets. The overall approach is readily automated, thus providing an initial critical information in determining optimal MV and PEEP settings. Such a model could also be used to validate the prediction accuracy of other more complex models and improve the confidence of their clinical application. Most importantly, the ability to predict PIP and overall pressure trajectories provide a means to safely titrate PEEP to optimal levels without the necessary to build an accurate enough mathematical model which could cost plenty of time and effort.

UKF-SVM Based Generalized Predictive Control of Multicompartment Lung Mechanics Model

In this paper, least-squares support vector machine (LS-SVM), whose parameters are updated by unscented Kalman filter (UKF), is adopted in the generalized predictive control (GPC) of a system with general multicompartment lung mechanics. Gaussian kernel function is employed since it presents a good approximation to the inner product of nonlinear mapping possessed in the SVM formulation. In the SVM literature, it is well known that the width parameter σ of the Gaussian kernel function has an important effect on the performance. However, it is not possible to train that parameter together with the other parameters of SVM when using linear least squares. This is why we use UKF for parameter adaptation in the SVM formulation. At each time instant of the control task, all parameters of the LS-SVM model, including σ, are tuned simultaneously. Another reason to employ UKF is; it avoids the suboptimal solutions caused by linearization based filters, e.g., extended Kalman filter. Due to these facts, we train the SVM model using UKF and it will be referred to as the UKF-SVM model. Simulation results concerning the application of UKF-SVM based GPC to a multicompartment lung mechanics model yields plausible performance using small amount of support vectors even when there are time-varying lung parameters and disturbance of high level affecting the system. The adopted approach can also be useful when there is not any knowledge of the system dynamics, i.e., black box. Note that, multicompartment lung mechanics system is a stand-in model that can mimic the behavior of human lung. Thus, it is appropriate for hardware-in-the-loop simulation which opens a path to the real-patient-tests of mechanical respiratory systems in the future.

Accuracy of Reported Tidal Volume During Neonatal Ventilation With Airway Leak: A Lung Model Study.

To determine the accuracy of tidal volume reported by neonatal ventilators, with and without leak compensation, in invasive and noninvasive ventilation modes in the presence of airway leak; and, to determine what factors have a significant effect on the accuracy of tidal volume reported by ventilators with leak compensation in the presence of airway leak. We hypothesized that ventilators with a leak compensation function that includes estimation of tidal volume could accurately report tidal volume in the presence of airway leak, but that the accuracy of reported tidal volume may be affected by variables such as the identity of the ventilator, lung mechanics, leak size, positive end-expiratory pressure level, and body size. In vitro assessment of ventilator volume delivery was conducted for seven acute care ventilators using a passive lung simulator. Laboratory-based measurements. The error of reported tidal volume was calculated under three ventilation modes (noninvasive-pressure-control, invasive-pressure-control, and invasive-dual-control ventilation), three models of lung mechanics (normal and restrictive and obstructive lung disease), a range of airway leak values, two positive end-expiratory pressure values, and two body weights for each ventilator. Ventilators with and without leak compensation were studied. In the absence of airway leak, all ventilators reported tidal volume accurately. In the presence of airway leak, the error of reported tidal volume increased for all ventilators without a leak compensation algorithm while ventilators with leak compensation that included estimation of tidal volume accurately reported tidal volume. In the presence of airway leak, clinically significant effects on the error of reported tidal volume by ventilators with leak compensation were associated with the choice of ventilator in all modes and with lung mechanics in invasive ventilation modes. Reported tidal volume is affected by the presence of airway leak, but in many ventilators a leak compensation algorithm that includes estimation of tidal volume can correct for the discrepancy between actual and reported tidal volume. However, even in ventilators with leak compensation, choice of ventilator and lung mechanics in invasive ventilation modes have a significant effect on error of reported tidal volume.

Predicting the Effects of Changing PEEP Using a Basis Function Method

Current methods to optimise mechanical ventilation involve increasing positive end expiratory pressure (PEEP) in steps to maximize recruitment. If PEEP is too high, overdistension and damage occur. There is thus an inherent risk involved when increasing PEEP. This study predicts dynamic elastance and lung mechanics for higher PEEP using clinically relevant elastance basis functions, capturing distension, recruitment and constant stiffness, in a first order model of lung mechanics. The clinically relevant basis functions were used to fit elastance using a single compartment lung model for 10 patients undergoing recruitment maneuvers, where 2-4 PEEP levels were analysed, and then used to predict the elastance and pressure waveforms for PEEP level increases of 5 and 10 cmH2O. The mean error for the pressure fits from the clinically relevant basis functions was 2.06%. Mean error for pressure predictions with a PEEP level increase of 5 cmH2O was 3.8-5.5%. Mean error for PEEP level increases of 10 cmH2O was slightly higher, between 5.0 and 6.6%. Good pressure fits and predictions show these basis functions accurately fit and predict elastance and thus lung behavior at increased PEEP levels. Each clinically relevant basis function behaved as expected, however improvements to the identifiability of distension would further improve the overall accuracy.

A New Lung Mechanics Model and Its Evaluation with Clinical Data

Acute Respiratory Distress Syndrome (ARDS) is a major cause of morbidity and has a high rate of mortality. ARDS patients in the intensive care unit (ICU) require mechan-ical ventilation (MV) for breathing support, but inappropriate settings of MV can lead to ventilator induced lung injury (VILI). Those complications may be avoided by carefully optimizing ventilation parameters through model-based approaches. In this study we introduced a new model of lung mechanics (mNARX) which is a variation of the NARX model by Langdon et al. A multivariate process was undertaken to deter-mine the optimal parameters of the mNARX model and hence, the final structure of the model fit 25 patient data sets and successfully described all parts of the breathing cycle. The model was highly successful in predicting missing data and showed minimal error. Thus, this model can be used by the clinicians to find the optimal patient specific ventilator settings.

Prospective evaluation of a decision support system providing advice on pressure support from states of over- and under-support

Providing appropriate pressure support (PS) is a balance of avoiding over-support with risk of muscle atrophy and prolonged weaning, and under-support with risk of patient discomfort and stress. The Beacon Caresystem (Mermaid Care, Denmark) advises on PS using physiological models of lung mechanics, respiratory drive, acid-base status and muscle function and clinical preference functions quantifying risk of muscle atrophy, patient stress, and lung trauma. Mathematical models are tuned to measurements allowing advice to be patient specific.

Adaptive sliding mode control for a general nonlinear multicompartment lung model with input pressure and rate saturation constraints

SummaryIn this article, we develop an output feedback adaptive sliding mode controller for a general multicompartment lung mechanics model with nonlinear resistance and compliance respiratory parameters. Specifically, for a given clinically plausible reference volume pattern, we develop an adaptive sliding mode controller that accounts for input pressure and rate saturation constraints to automatically adjust the applied input pressure by the mechanical ventilator so that the total lung volume tracks the given reference pattern. The pressure due to lung muscle activity is also considered in the controller design. A Lyapunov‐based approach is presented to show convergence of the closed‐loop system, and the proposed control framework is applied to a two‐compartment lung model to show the efficacy of the proposed control method. Copyright © 2015 John Wiley & Sons, Ltd.

The nonallergic asthma of obesity. A matter of distal lung compliance.

The pathogenesis of asthma in obesity is poorly understood, but may be related to breathing at low lung volumes. To determine if lung function in obese patients with asthma and control subjects would respond differently to weight loss. Lung function was evaluated by conventional clinical tests and by impulse oscillometry in female late-onset, nonallergic patients with asthma and control subjects before, and 12 months after, bariatric surgery. Patients with asthma (n = 10) had significantly lower FEV1 (79.8 ± 10.6 vs. 95.5 ± 7.0%) and FVC (82.4 ± 13.2 vs. 93.7 ± 8.9%) compared with control subjects (n = 13). There were no significant differences in FRC or TLC at baseline. Twelve months after surgery, control subjects had significant increases in FEV1 (95.5 ± 7.0 to 100.7 ± 5.9), FVC (93.6 ± 8.9 to 98.6 ± 8.3%), FRC (45.4 ± 18.5 to 62.1 ± 15.3%), and TLC (84.8 ± 15.0 to 103.1 ± 15.3%), whereas patients with asthma had improvement only in FEV1 (79.8 ± 10.6 to 87.2 ± 11.5). Control subjects and patients with asthma had a significantly different change in respiratory system resistance with weight loss: control subjects exhibited a uniform decrease in respiratory system resistance at all frequencies, whereas patients with asthma exhibited a decrease in frequency dependence of resistance. Fits of a mathematical model of lung mechanics to these impedance spectra suggest that the lung periphery was more collapsed by obesity in patients with asthma compared with control subjects. Weight loss decompresses the lung in both obese control subjects and patients with asthma, but the more pronounced effects of weight loss on lung elastance suggest that the distal lung is inherently more collapsible in people with asthma.

Predictive tracking control for a multi-compartment respiratory system with amplitude and rate input constraints

In this article, we develop a predictive tracking controller for a nonlinear multi-compartment lung mechanics model. Specifically, for a given clinically plausible reference volume pattern, we use model predictive control to design a tracking controller that accounts for amplitude and rate input constraints. The predictive control law is derived by minimising a quadratic performance criterion involving a prediction of the system response over a prescribed time step. The proposed tracking control framework is applied to a two-compartment lung mechanics model with nonlinear lung compliance parameters.

Model Predictive Control for a Multicompartment Respiratory System

In this brief, we develop a model predictive controller for a multicompartment respiratory system. Specifically, for a given periodic reference volume pattern, we design a tracking controller using a framework that merges repetitive control and model predictive control. In particular, the periodic multicompartment model is transformed into a lifted run-to-run invariant model, and a model predictive controller with nonnegative control input constraints is designed. The proposed tracking control framework is applied to a two-compartment lung mechanics model to demonstrate the efficacy of the proposed approach.

Hyperpolarized 3He magnetic resonance imaging-derived pulmonary pressure-volume curves

We aimed to evaluate the potential for the use of hyperpolarized helium-3 magnetic resonance imaging (MRI) apparent diffusion coefficient (ADC) surrogates of alveolar size, together with literature-based morphological parameters in a theoretical model of lung mechanics to simulate noninvasive transpulmonary pressure-volume curves. Fourteen ex-smokers with chronic obstructive pulmonary disease (COPD) (n = 8 stage II, n = 6 stage III/IV COPD) and five age-matched never-smokers, provided written, informed consent and were evaluated at baseline and 26 + or - 2 mo later (n = 15 subjects) using plethysmography, spirometry, and (3)He MRI at 3.0 T. Total lung capacity, residual volume, and literature-based morphological parameters were used with alveolar volumes derived from (3)He ADC to simulate noninvasive pressure-volume curves. The resultant anterior-posterior transpulmonary pressure gradient was significantly decreased for stage II COPD (P < 0.01) and stage III COPD subjects (P < 0.001) compared with healthy volunteers. Both COPD subgroups showed increased alveolar radius compared with healthy subjects (P < 0.01, stage II COPD; P < 0.001, stage III COPD). In addition, surface area and surface tension were significantly increased in stage III COPD compared with healthy volunteers (P < 0.01). These results suggest that (3)He MRI provides a potential noninvasive approach to evaluate lung mechanics regionally and further supports the use of ADC values as a regional noninvasive probe of pulmonary microstructure and compliance.