Strenuous respiratory effort has been proposed as a second hit for the lungs, labeled as “patient self-inflicted lung injury” (P-SILI). This secondary exploratory analysis evaluated whether insufficient respiratory support is associated with biventricular myocardial injury and pulmonary microvascular ultrastructural changes in experimental acute lung injury. In rats, lung injury was induced through surfactant depletion, followed by 3 h of unsupported (standard oxygen therapy, 2L/min) or supported ventilation: protective mechanical ventilation (MV), continuous positive airway pressure (CPAP) 6 cmH2O, or high-flow nasal oxygen (HFNO) 4L/min. Subjects were assessed through general monitoring, arterial blood gas analysis, esophageal manometry (inspiratory effort), surface electromyography on the abdominal wall (expiratory effort), echocardiography (right ventricular outflow), and coagulation dysfunction analysis. An exploratory histological hypothesis-generating study on the right and left ventricular (RV and LV) myocardium was performed in all animals, and a quantitative scanning electron microscopy-based analysis of luminal permeability in small pulmonary vessels was completed only in four subjects per injured group. The primary endpoint was RV myocardial injury, while LV injury and pulmonary microvascular changes were considered secondary endpoints. The unsupported group presented higher heart rate, hypercapnia, increased arterial lactate levels, respiratory variation in pulmonary peak flow velocity, RV and LV myocardial injury, and incipient alterations in coagulation compared to MV and CPAP (all p < 0.05). All support therapies were associated with lower myocardial injury score: MV and CPAP showed the lowest RV injury, and HFNO the highest. Only MV was strongly associated with lower LV injury score (all p < 0.05). The Standard oxygen therapy and HFNO groups showed significantly higher luminal occlusion in small pulmonary vessels, consisting of erythrocyte-rich intravascular aggregates and amorphous material adherent to the vascular wall (all p < 0.05). Unsupported respiratory effort was associated with biventricular myocardial injury and pulmonary microvascular ultrastructural findings characterized by occlusive intravascular aggregates compatible with early microthrombotic changes. These findings support the concept that underassistance injury may extend beyond the lung parenchyma and respiratory muscles to involve cardiovascular and pulmonary microvascular domains.

Strenuous respiratory effort has been proposed as a second hit, introducing the concepts of “patient self-inflicted lung injury” (P-SILI) and underassistance myotrauma. We investigated the effects of continuous positive airway pressure (CPAP) and high-flow nasal oxygen (HFNO) on inspiratory and expiratory effort, lung injury, and myotrauma of obligatory and accessory respiratory muscles in experimental P-SILI. In rats, lung injury was induced through surfactant depletion, followed by 3 h of standard oxygen therapy (2 L/min, positive control) or supported ventilation (CPAP 6 cmH2O, HFNO 4 L/min, and protective mechanical ventilation [MV, negative control] groups). Subjects were assessed through esophageal manometry, surface electromyography on the abdominal wall, thoracic ultrasound, plasma multiplex analysis, and histology on the lungs, diaphragm, and accessory inspiratory and expiratory muscles. Compared against standard oxygen therapy, the MV and CPAP groups resulted in lower inspiratory and expiratory efforts and diaphragm excursion. Furthermore, intragroup analysis showed that CPAP also reduced respiratory rate and prevented the loss of lung aeration. All support therapies prevented lung, diaphragm, and accessory muscle injuries, but with nuances. In lungs and accessory muscles, MV showed the highest injury protection and HFNO the lowest, while CPAP was the most protective for the diaphragm. Only MV resulted in a lower plasma GRO-α concentration than standard oxygen therapy. Invasive and non-invasive respiratory support therapies are effective in protecting the lungs and respiratory muscles, but the effect is stepped in the lung and varies between obligatory and accessory respiratory muscles.

Deformable Image Registration (DIR) is a fundamental problem in biomedical imaging that analyzes organ displacements and deformations by comparing images from different states. DIR becomes particularly challenging when analyzing images that display non-affine large deformations or noisy image data. In this study, two novel similarity measures for DIR based on the Radon Transform (RT) are introduced, which, together with a linear elastic regularizer, define the formulation of the proposed projection-based DIR models. We present a theoretical analysis of the proposed RT-DIR formulation, providing conditions for the existence and uniqueness of solutions in the continuous case. The proposed RT-DIR methods are implemented using a finite-element-based deformation system and a gradient-informed quasi-Newton optimization algorithm. We compare the performance of these methods against a traditional DIR approach that employs the Sum of Squared Differences (SSD) as the similarity measure. Experimental tests include synthetic random non-affine deformations with varying noise levels and a case of real lung deformation. We show that the RT-based models exhibit enhanced accuracy in capturing non-affine deformations, higher robustness to noise, and a significantly faster convergence rate compared to the SSD-based method. We further demonstrate the applicability of the RT-DIR method on human lung images.

Surface tension arising in the air-liquid interface of alveoli is a fundamental mechanism lung physiology that explains lung recoil and hysteresis during breathing. However, pulmonary surface tension is typically neglected in continuum models of the lungs, possibly due to complex multiscale physicochemical nature. In this study, we formulate a poromechanical framework that incorporates the effect of surfactant-dependent surface tension in porous for the prediction of lung hysteretic response. Using an internal variable formalism, we the Coleman-Noll procedure to establish an expression for the stress tensor that includes surface tension akin to the Young-Laplace law. Based on this formulation, we construct non-linear finite-element model of human lungs to simulate pressure-volume curves and response during mechanical ventilation. Our results show that surfactant-dependent tension notably modulates pressure-volume curves and lung mechanics. In particular, our captures the influence of surfactant dynamics on lung hysteresis and compliance, predicting the transition from an insoluble reversible regime to a dissipative one governed by Langmuir kinetics. We envision that our continuum framework will enable lung simulations surfactant-related phenomena are directly considered in predictions, with important applications to modeling respiratory disease and lung response to mechanical ventilation.

IntroductionDigital twins of the respiratory system have shown promise in predicting the patient-specific response of lungs connected to mechanical ventilation. However, modeling the spatiotemporal response of the lung tissue through high-fidelity numerical simulations involves computing times that largely exceed those required in clinical applications. In this work, we present a multi-fidelity deep learning surrogate model to efficiently and accurately predict the poromechanical fields that arise in lungs connected to mechanical ventilation.MethodsWe generate training datasets with two fidelity levels from non-linear finite-element simulations on coarse (low-fidelity) and fine (high-fidelity) discretizations of the lungs domain. Further, we reduce the output spatiotemporal dimensionality using singular value decomposition, capturing over 99% of the variance in both displacement and alveolar pressure fields with only a few principal components. Based on this procedure, we learn both the input-output mappings and fidelity correlations by training a reduced-order multi-fidelity neural network model (rMFNN) that leverages the abundant low-fidelity data to enhance predictions from scarce high-fidelity simulations.ResultsCompared to a reduced-order single-fidelity neural network (rSFNN) surrogate, the rMFNN achieves superior predictive accuracy in predicting spatiotemporal displacement and alveolar pressure fields (R2 ≥ 93% (rMFNN) vs R2 ≥ 75% (rSFNN)). In addition, we show that rMFNN outperforms rSFNN in terms of accuracy for the same level of training cost. Further, the rMFNN model provides inference times of less than a minute, offering speed-ups up to 462× when compared to finite-element numerical simulations.DiscussionThese results demonstrate the potential of the rMFNN lung model to enable patient-specific predictions in acceptable computing times that can be used to personalize mechanical ventilation therapy in critical patients.

Pulmonary capillary perfusion and gas exchange are physiological processes that take place at the alveolar level and that are fundamental to sustaining life. Present-day computational simulations of these phenomena are based on low-dimensional mathematical models solved in idealized alveolar geometries, where the chemical reactions between O-2-CO2 and hemoglobin are simplified. While providing general insights, current modeling efforts fail to capture the complex chemical reactions that take place in pulmonary capillary blood flow on arbitrary geometries and ignore the crucial impact of microstructural morphology on pulmonary function. Here, we propose a coupled continuum perfusion and gas exchange model that captures complex gas and hemoglobin dynamics in realistic geometries of alveolar tissue. To this end, we derive appropriate governing equations incorporating a two-way Hill-like relationship between gas partial pressures and hemoglobin saturations. We numerically solve the resulting boundary-value problem using a non-linear finite-element approach to simulate and validate velocity, partial pressure, and hemoglobin saturation fields in simple geometries. We further perform sensitivity studies to understand the impact of blood speed and acidity variability on key physiological fields. Notably, we simulate perfusion and gas exchange on anatomical alveolar domains constructed from 3D mu-computed-tomography images of murine lungs. Based on these models, we show that morphological variations decrease O-2 and CO2 diffusing capacity, predicting trends and values that are consistent with current medical knowledge. We envision that our model will provide an effective in silico framework to study how exercise and pathological conditions affect perfusion dynamics and the overall gas exchange function of the respiratory system.

Strenuous respiratory effort has been proposed as a second hit in severe acute lung injury (ALI), introducing the concept of “patient self-inflicted lung injury” (P-SILI). In an experimental setting, noninvasive continuous positive airway pressure (CPAP) attenuates lung and diaphragmatic injury, but the underlying mechanisms remains elusive. Here we investigate the effects of noninvasive CPAP on global and regional lung strain and diaphragm velocity of contraction and relaxation in an experimental P-SILI model. Lung injury was induced in Sprague Dawley rats through surfactant depletion followed by either three hours of standard oxygen therapy (Control group) or CPAP support (CPAP group). Subjects were assessed through inspiratory and expiratory muscle activation. Regional lung and diaphragmatic deformation amplitude (strain) and the rate of change (strain rate) maps were developed using a micro-computed tomography (µCT) scan. Morphometric tissue assessment was carried out to study biological damage. Compared with the Control group, the CPAP group resulted in: (1) higher SpO2 and lower respiratory rate, nasal flaring, inspiratory and expiratory muscle activation, and minute ventilation at the end of the study; (2) lower global and regional tidal ventilation at the beginning of the study; (3) lower regional inspiratory and expiratory lung strain rate over time; and (4) higher muscle area in the diaphragm morphometric analysis. Furthermore, intragroup analysis showed that only the CPAP group reduced the inspiratory and expiratory muscle activation, the global and regional expiratory lung strain rate and the regional velocity of relaxation of the diaphragm over time. Standard oxygen therapy resulted in worse patterns of lung strain rate and diaphragm velocity of relaxation, consistent with P-SILI and load-induced diaphragm injury. CPAP resulted in improved lung function, decreased lung strain rate, and diaphragmatic relaxation velocity throughout the respiratory cycle. We conclude that CPAP promotes biomechanical protection in injured lungs and diaphragm, more noticeably during the expiratory phase.

Abstract Background The spatiotemporal progression and patterns of tissue deformation in ventilator-induced lung injury (VILI) remain understudied. Our aim was to identify lung clusters based on their regional mechanical behavior over space and time in lungs subjected to VILI using machine-learning techniques. Results Ten anesthetized pigs (27 ± 2 kg) were studied. Eight subjects were analyzed. End-inspiratory and end-expiratory lung computed tomography scans were performed at the beginning and after 12 h of one-hit VILI model. Regional image-based biomechanical analysis was used to determine end-expiratory aeration, tidal recruitment, and volumetric strain for both early and late stages. Clustering analysis was performed using principal component analysis and K-Means algorithms. We identified three different clusters of lung tissue: Stable, Recruitable Unstable, and Non-Recruitable Unstable. End-expiratory aeration, tidal recruitment, and volumetric strain were significantly different between clusters at early stage. At late stage, we found a step loss of end-expiratory aeration among clusters, lowest in Stable, followed by Unstable Recruitable, and highest in the Unstable Non-Recruitable cluster. Volumetric strain remaining unchanged in the Stable cluster, with slight increases in the Recruitable cluster, and strong reduction in the Unstable Non-Recruitable cluster. Conclusions VILI is a regional and dynamic phenomenon. Using unbiased machine-learning techniques we can identify the coexistence of three functional lung tissue compartments with different spatiotemporal regional biomechanical behavior.

Irreversible alveolar airspace enlargement is the main characteristic of pulmonary emphysema, which has been extensively studied using animal models. While the alterations in lung mechanics associated with these morphological changes have been documented in the literature, the study of the mechanical behavior of parenchymal tissue from emphysematous lungs has been poorly investigated. In this work, we characterize the mechanical and morphological properties of lung tissue in elastase-induced emphysema rat models under varying severity conditions. We analyze the non-linear tissue behavior using suitable hyperelastic constitutive models that enable to compare different non-linear responses in terms of hyperelastic material parameters. We further analyze the effect of the elastase dose on alveolar morphology and tissue material parameters and study their connection with respiratory-system mechanical parameters. Our results show that while the lung mechanical function is not significantly influenced by the elastase treatment, the tissue mechanical behavior and alveolar morphology are markedly affected by it. We further show a strong association between alveolar enlargement and tissue softening, not evidenced by respiratory-system compliance. Our findings highlight the importance of understanding tissue mechanics in emphysematous lungs, as changes in tissue properties could detect the early stages of emphysema remodeling. STATEMENT OF SIGNIFICANCE: Gas exchange is vital for life and strongly relies on the mechanical function of the lungs. Pulmonary emphysema is a prevalent respiratory disease where alveolar walls are damaged, causing alveolar enlargement that induces harmful changes in the mechanical response of the lungs. In this work, we study how the mechanical properties of lung tissue change during emphysema. Our results from animal models show that tissue properties are more sensitive to alveolar enlargement due to emphysema than other mechanical properties that describe the function of the whole respiratory system.

"Non-Invasive CPAP Is a Lung- and Diaphragm-protective Approach in Self-inflicted Lung Injury." American Journal of Respiratory and Critical Care Medicine, 0(ja), pp.

Background and Objective: Traditional assessment of patient response in mechanical ventilation relies on respiratory-system compliance and airway resistance. Clinical evidence has shown high variability in these parameters, highlighting the difficulty of predicting them before the start of ventilation therapy. This motivates the creation of computational models that can connect structural and tissue features with lung mechanics. In this work, we leverage machine learning (ML) techniques to construct predictive lung function models informed by non-linear finite element simulations, and use them to investigate the propagation of uncertainty in the lung mechanical response.Methods: We revisit a continuum poromechanical formulation of the lungs suitable for determining patient response. Based on this framework, we create high-fidelity finite element models of human lungs from medical images. We also develop a low-fidelity model based on an idealized sphere geometry. We then use these models to train and validate three ML architectures: single-fidelity and multi-fidelity Gaussian process regression, and artificial neural networks. We use the best predictive ML model to further study the sensitivity of lung response to variations in tissue structural parameters and boundary conditions via sensitivity analysis and forward uncertainty quantification. Codes are available for download at https://github .com /comp-medicine-uc /ML-lung mechanics -UQResults: The low-fidelity model delivers a lung response very close to that predicted by high-fidelity simulations and at a fraction of the computational time. Regarding the trained ML models, the multi-fidelity GP model consistently delivers better accuracy than the single-fidelity GP and neural network models in estimating respiratory-system compliance and resistance (������2 similar to 0.99). In terms of computational efficiency, our ML model delivers a massive speed-up of similar to 970, 000x with respect to high-fidelity simulations. Regarding lung function, we observed an almost matched and non-linear behavior between specific structural parameters and chest wall stiffness with compliance. Also, we observed a strong modulation of airways resistance with tissue permeability.Conclusions: Our findings unveil the relevance of specific lung tissue parameters and boundary conditions in the respiratory-system response. Furthermore, we highlight the advantages of adopting a multi-fidelity ML approach that combines data from different levels to yield accurate and efficient estimates of clinical mechanical markers. We envision that the methods presented here can open the way to the development of predictive ML models of the lung response that can inform clinical decisions.

Deformable image registration is a standard engineering problem used to determine the distortion experienced by a body by comparing two images of it in different states. This study introduces two new DIR methods designed to capture non-affine deformations using Radon transform-based similarity measures and a classical regularizer based on linear elastic deformation energy. It establishes conditions for the existence and uniqueness of solutions for both methods and presents synthetic experimental results comparing them with a standard method based on the sum of squared differences similarity measure. These methods have been tested to capture various non-affine deformations in images, both with and without noise, and their convergence rates have been analyzed. Furthermore, the effectiveness of these methods was also evaluated in a lung image registration scenario.

The diagnosis of heart failure usually includes a global functional assessment, such as ejection fraction measured by magnetic resonance imaging. However, these metrics have low discriminate power to distinguish different cardiomyopathies, which may not affect the global function of the heart. Quantifying local deformations in the form of cardiac strain can provide helpful information, but it remains a challenge. In this work, we introduce WarpPINN, a physics-informed neural network to perform image registration to obtain local metrics of heart deformation. We apply this method to cine magnetic resonance images to estimate the motion during the cardiac cycle. We inform our neural network of the near-incompressibility of cardiac tissue by penalizing the Jacobian of the deformation field. The loss function has two components: an intensity-based similarity term between the reference and the warped template images, and a regularizer that represents the hyperelastic behavior of the tissue. The architecture of the neural network allows us to easily compute the strain via automatic differentiation to assess cardiac activity. We use Fourier feature mappings to overcome the spectral bias of neural networks, allowing us to capture discontinuities in the strain field. The algorithm is tested on synthetic examples and on a cine SSFP MRI benchmark of 15 healthy volunteers, where it is trained to learn the deformation mapping of each case. We outperform current methodologies in landmark tracking and provide physiological strain estimations in the radial and circumferential directions. WarpPINN provides precise measurements of local cardiac deformations that can be used for a better diagnosis of heart failure and can be used for general image registration tasks. Source code is available at https://github.com/fsahli/WarpPINN.

The mechanical behavior of the lungs has long been associated with the structural properties of alveoli in pulmonary medicine. However, this structure-function relationship has mostly been qualitative, as experimentation in real lungs is costly and limited by ethical standards. Here we present a poromechanical multiscale model that connects key alveolar features with organ-level function. To this end, we first revisit an asymptotic homogenization framework for finite-deformation poromechanics and formulate fine-scale and coarse-scale problems that govern lung mechanics. We further inform the coarse-scale problem using a tetrakaidecahedron micromechanical model for the alveolar response at the fine scale that strongly depends on the alveolar-wall elastic modulus and the initial alveolar porosity. Based on this formulation, we construct a non-linear finite element model from anatomical geometries to simulate the response of human lungs connected to a mechanical ventilator under pressure-controlled and volume-controlled protocols. We show that the predicted signals for airway pressure, airway flow, and lung volume capture the dynamic waveform characteristics observed in human lungs. Further, we demonstrate that lung behavior, measured in terms of respiratory-system compliance, strongly depends on the alveolar-wall elasticity and alveolar porosity. In particular, we show that variations in these microstructural parameters result in marked changes in compliance that follow the structure-function relations observed in lungs with pulmonary fibrosis and emphysema, two prevalent chronic respiratory diseases. We envision that our multiscale lung model can enhance current in silico efforts to experimentation in respiratory research and provide a computational framework for clinically-relevant simulations. Codes are available for download at https://github.com/comp-medicine-uc/multiscale-lung-mechanics.

Introduction: Chronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory disease that presents a high rate of underdiagnosis during onset and early stages. Studies have shown that in mild COPD patients, remodeling of the small airways occurs concurrently with morphological changes in the proximal airways. Despite this evidence, the geometrical study of the airway tree from computed tomography (CT) lung images remains underexplored due to poor representations and limited tools to characterize the airway structure.Methods: We perform a comprehensive morphometric study of the proximal airways based on geometrical measures associated with the different airway generations. To this end, we leverage the geometric flexibility of the Snakes IsoGeometric Analysis method to accurately represent and characterize the airway luminal surface and volume informed by CT images of the respiratory tree. Based on this framework, we study the airway geometry of smoking pre-COPD and mild COPD individuals.Results: Our results show a significant difference between groups in airway volume, length, luminal eccentricity, minimum radius, and surface-area-to-volume ratio in the most distal airways.Discussion: Our findings suggest a higher degree of airway narrowing and collapse in COPD patients when compared to pre-COPD patients. We envision that our work has the potential to deliver a comprehensive tool for assessing morphological changes in airway geometry that take place in the early stages of COPD.

Geothermal systems are commonly genetically and spatially associated with volcanic complexes, which in turn, are located nearby crustal fault systems. Faults can alter fluid flow in their surroundings, potentially acting as barriers or conduits for fluids, depending on their architecture and slip-rate. However, this fundamental control on fluid migration is still poorly constrained. Most previous modeling efforts on volcanic and hydrothermal processes consider either only fluid flow in their formulations, or only a mechanical approach, and seldom a full, monolithic coupling between both. In this work, we present a poro-elasto-plastic Finite Element Method (FEM) to address the first-order, time-dependent control that a strike-slip crustal fault exerts on a nearby geothermal reservoir. For the model setting, we selected the Planchón-Peteroa geothermal system in the Southern Andes Volcanic Zone (SAVZ), for which the geometry and kinematics of a potentially seismogenic fault and fluid reservoir is constrained from previous geological and geophysical studies. We assess the emergence and diffusion of fluid pressure domains due to fault slip, as well as the development of tensile/dilational and compressive/contractional domains in the fault' surroundings. Mean stress and volumetric strain magnitudes in these domains range between ±1 [MPa] and ±10−4 [-], respectively. Our results show the appearance of negative and positive fluid pressure domains in these dilational and contractional regions, respectively. We also investigate the spatial and temporal evolution of such domains resulting from changes in fault permeability and shear modulus, fluid viscosity, and rock rheology. These variations in fluid pressure alter the trajectory of the reservoir fluids, increasing migration to the eastern half of the fault, reaching a maximum fluid flux of 8 to 70 times the stationary flux. Pressure-driven fluid diffusion over time causes fluid flow to return to the stationary state between weeks to months after fault slip. These results suggest that the mechanism that exerts a first-order control is similar to a suction pump, whose duration heavily depends on fault permeability and fluid viscosity. We also show how a von Mises plasticity criterion locally enhances fluid flow. The transient process analyzed in this work highlights the importance of addressing the solid-fluid coupling in numerical models for volcano-tectonic studies.

Background and Purpose Prognostic models based on cardiovascular hemodynamic parameters may bring new information for an early assessment of patients with bicuspid aortic valve (BAV), playing a key role in reducing the long-term risk of cardiovascular events. This work quantifies several three-dimensional hemodynamic parameters in different patients with BAV and ranks their relationships with aortic diameter. Materials and Methods Using 4D-flow CMR data of 74 patients with BAV (49 right-left and 25 right-non-coronary) and 48 healthy volunteers, aortic 3D maps of seventeen 17 different hemodynamic parameters were quantified along the thoracic aorta. Patients with BAV were divided into two morphotype categories, BAV-Non-AAoD (where we include 18 non-dilated patients and 7 root-dilated patients) and BAV-AAoD (where we include the 49 patients with dilatation of the ascending aorta). Differences between volunteers and patients were evaluated using MANOVA with Pillai's trace statistic, Mann–Whitney U test, ROC curves, and minimum redundancy maximum relevance algorithm. Spearman's correlation was used to correlate the dilation with each hemodynamic parameter. Results The flow eccentricity, backward velocity, velocity angle, regurgitation fraction, circumferential wall shear stress, axial vorticity, and axial circulation allowed to discriminate between volunteers and patients with BAV, even in the absence of dilation. In patients with BAV, the diameter presented a strong correlation (> |+/−0.7|) with the forward velocity and velocity angle, and a good correlation (> |+/−0.5|) with regurgitation fraction, wall shear stress, wall shear stress axial, and vorticity, also for morphotypes and phenotypes, some of them are correlated with the diameter. The velocity angle proved to be an excellent biomarker in the differentiation between volunteers and patients with BAV, BAV morphotypes, and BAV phenotypes, with an area under the curve bigger than 0.90, and higher predictor important scores. Conclusions Through the application of a novel 3D quantification method, hemodynamic parameters related to flow direction, such as flow eccentricity, velocity angle, and regurgitation fraction, presented the best relationships with a local diameter and effectively differentiated patients with BAV from healthy volunteers.

Abnormal deformation of blood vessels has been related to the onset and progression of prevalent cardiovascular diseases. This mechanical connection has motivated the development of computational techniques to assess strain fields in the wall of the aorta from medical images. In this work, we present the mechanics-informed snakes isogeometric analysis (MISIGA) method, which provides seamless 3D estimations of strain fields in the full aorta from magnetic resonance images. Our approach leverages image segmentation formulations with advanced curvilinear representations of irregular vessels to capture the deformation mapping between two configurations captured by image datasets. We further inform this model by describing the motion of the aortic wall based on a Kirchhoff-Love shell approach, which allows us to construct continuous circumferential and longitudinal strain fields in the full aorta. We validate the MISIGA method using synthetically generated images from aortic mechanical simulations, obtaining errors in the strain estimation of 13.2 and 9.8 for the circumferential and longitudinal components. This performance compares favorably with other approaches that are not informed by mechanical considerations. Further, we apply the MISIGA method in the strain assessment of the aorta of a normal subject, which results in longitudinal and circumferential strain values that are in the range of those found in previous studies. We envision that the MISIGA method can open the way to seamless 3D high-fidelity analysis of local strain from medical images of the aorta and other vessels.

The present study describes the magnitude and spatial distribution of lung strain in healthy anesthetized, mechanically ventilated dogs with and without positive end-expiratory pressure (PEEP). Total lung strain (LSTOTAL) has a dynamic (LSDYNAMIC) and a static (LSSTATIC) component. Due to lung heterogeneity, global lung strain may not accurately represent regional total tissue lung strain (TSTOTAL), which may also be described by a regional dynamic (TSDYNAMIC) and static (TSSTATIC) component. Six healthy anesthetized beagles (12.4 ± 1.4 kg body weight) were placed in dorsal recumbency and ventilated with a tidal volume of 15 ml/kg, respiratory rate of 15 bpm, and zero end-expiratory pressure (ZEEP). Respiratory system mechanics and full thoracic end-expiratory and end-inspiratory CT scan images were obtained at ZEEP. Thereafter, a PEEP of 5 cmH2O was set and respiratory system mechanics measurements and end-expiratory and end-inspiratory images were repeated. Computed lung volumes from CT scans were used to evaluate the global LSTOTAL, LSDYNAMIC, and LSSTATIC during PEEP. During ZEEP, LSSTATIC was assumed zero; therefore, LSTOTAL was the same as LSDYNAMIC. Image segmentation was applied to CT images to obtain maps of regional TSTOTAL, TSDYNAMIC, and TSSTATIC during PEEP, and TSDYNAMIC during ZEEP. Compliance increased (p = 0.013) and driving pressure decreased (p = 0.043) during PEEP. PEEP increased the end-expiratory lung volume (p < 0.001) and significantly reduced global LSDYNAMIC (33.4 ± 6.4% during ZEEP, 24.0 ± 4.6% during PEEP, p = 0.032). LSSTATIC by PEEP was larger than the reduction in LSDYNAMIC; therefore, LSTOTAL at PEEP was larger than LSDYNAMIC at ZEEP (p = 0.005). There was marked topographic heterogeneity of regional strains. PEEP induced a significant reduction in TSDYNAMIC in all lung regions (p < 0.05). Similar to global findings, PEEP-induced TSSTATIC was larger than the reduction in TSDYNAMIC; therefore, PEEP-induced TSTOTAL was larger than TSDYNAMIC at ZEEP. In conclusion, PEEP reduced both global and regional estimates of dynamic strain, but induced a large static strain. Given that lung injury has been mostly associated with tidal deformation, limiting dynamic strain may be an important clinical target in healthy and diseased lungs, but this requires further study.

Gas exchange is an essential function of the respiratory system that couples fundamentally with perfusion in respiratory alveoli. Current mathematical formulations and computational models of these two phenomena rely on one-dimensional approximations that neglect the intricate volumetric microstructure of alveolar structures. In this work, we introduce a coupled three-dimensional computational model of pulmonary capillary perfusion and gas exchange that conforms to alveolar morphology. To this end, we derive non-linear partial differential equations and boundary conditions from physical principles that govern the behavior of blood and gases in arbitrary alveolar domains. We numerically solve the resulting formulation by proposing and implementing a non-linear finite-element scheme. Further, we carry out several numerical experiments to validate our model against one-dimensional simulations and demonstrate its applicability to morphologically-inspired geometries. Numerical simulations show that our model predicts blood pressure drops and blood velocities expected in the pulmonary capillaries. Moreover, we replicate partial pressure dynamics of oxygen and carbon dioxide reported in previous studies. This overall behavior is also observed in three-dimensional alveolar geometries, providing more detail associated with the spatial distribution of fields of interest and the influence of the shape of the domain. We envision that this model opens the door for enhanced in silico studies of gas exchange and perfusion on realistic geometries, coupled models of respiratory mechanics and gas exchange, and multi-scale analysis of lung function; furthering our understanding of lung physiology and pathology. Codes are available at https://github.com/comp-medicine-uc/num-model-alv-perfusion-gas-exchange.