Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses

A personalised computational model of the impact of COVID-19 on lung function under mechanical ventilation

This work proposes a modelling framework to analyse flow and pressure distributions throughout the lung of mechanically ventilated COVID-19 patients. The methodology involves: segmentation of the lungs and major airways from patient CT images; a volume filling algorithm that creates a dichotomous airway network in the remaining volume of the lung; an estimate of resistance and compliance within the lung based on Hounsfield unit values from the CT scan; and a computational fluid dynamics model to analyse flow, lung inflation, and pressure throughout the airway network. Mechanically ventilated patients with differing progression and severity of the disease were simulated. The results indicate that the flow distribution within the lung can be significantly affected when there are competing types of lung damage. These competing types are primarily fibrosis-like lung damage that creates higher resistance and lower compliance in that region; and emphysema, which causes a decrease in resistance and increase in compliance. In a patient with severe disease, the model predicted an increase in inflation by 33% in an area affected by emphysema-like conditions. This could increase the risk of alveolar rupture. The framework could readily be adapted to study other respiratory diseases. Early interventions in critical respiratory care could be facilitated through such efficient patient-specific modelling approaches.

Pressure- and time-dependent alveolar recruitment/derecruitment in a spatially resolved patient-specific computational model for injured human lungs

We present a novel computational model for the dynamics of alveolar recruitment/derecruitment (RD), which reproduces the underlying characteristics typically observed in injured lungs. The basic idea is a pressure- and time-dependent variation of the stress-free reference volume in reduced dimensional viscoelastic elements representing the acinar tissue. We choose a variable reference volume triggered by critical opening and closing pressures in a time-dependent manner from a straightforward mechanical point of view. In the case of (partially and progressively) collapsing alveolar structures, the volume available for expansion during breathing reduces and vice versa, eventually enabling consideration of alveolar collapse and reopening in our model. We further introduce a method for patient-specific determination of the underlying critical parameters of the new alveolar RD dynamics when integrated into the tissue elements, referred to as terminal units, of a spatially resolved physics-based lung model that simulates the human respiratory system in an anatomically correct manner. Relevant patient-specific parameters of the terminal units are herein determined based on medical image data and the macromechanical behavior of the lung during artificial ventilation. We test the whole modeling approach for a real-life scenario by applying it to the clinical data of a mechanically ventilated patient. The generated lung model is capable of reproducing clinical measurements such as tidal volume and pleural pressure during various ventilation maneuvers. We conclude that this new model is an important step toward personalized treatment of ARDS patients by considering potentially harmful mechanisms-such as cyclic RD and overdistension-and might help in the development of relevant protective ventilation strategies to reduce ventilator-induced lung injury (VILI).

An approach to study recruitment/derecruitment dynamics in a patient-specific computational model of an injured human lung

We present a new approach for physics-based computational modeling of diseased human lungs. Our main object is the development of a model that takes the novel step of incorporating the dynamics of airway recruitment/derecruitment into an anatomically accurate, spatially resolved model of respiratory system mechanics, and the relation of these dynamics to airway dimensions and the biophysical properties of the lining fluid. The importance of our approach is that it potentially allows for more accurate predictions of where mechanical stress foci arise in the lungs, since it is at these locations that injury is thought to arise and propagate from. We match the model to data from a patient with acute respiratory distress syndrome (ARDS) to demonstrate the potential of the model for revealing the underlying derangements in ARDS in a patient-specific manner. To achieve this, the specific geometry of the lung and its heterogeneous pattern of injury are extracted from medical CT images. The mechanical behavior of the model is tailored to the patient's respiratory mechanics using measured ventilation data. In retrospective simulations of various clinically performed, pressure-driven ventilation profiles, the model adequately reproduces clinical quantities measured in the patient such as tidal volume and change in pleural pressure. The model also exhibits physiologically reasonable lung recruitment dynamics and has the spatial resolution to allow the study of local mechanical quantities such as alveolar strains. This modeling approach advances our ability to perform patient-specific studies in silico, opening the way to personalized therapies that will optimize patient outcomes.

Computational lung modelling in respiratory medicine

Computational modelling of the lungs is an active field of study that integrates computational advances with lung biophysics, biomechanics, physiology and medical imaging to promote individualized diagnosis, prognosis and therapy evaluation in lung diseases. The complex and hierarchical architecture of the lung offers a rich, but also challenging, research area demanding a cross-scale understanding of lung mechanics and advanced computational tools to effectively model lung biomechanics in both health and disease. Various approaches have been proposed to study different aspects of respiration, ranging from compartmental to discrete micromechanical and continuum representations of the lungs. This article reviews several developments in computational lung modelling and how they are integrated with preclinical and clinical data. We begin with a description of lung anatomy and how different tissue components across multiple length scales affect lung mechanics at the organ level. We then review common physiological and imaging data acquisition methods used to inform modelling efforts. Building on these reviews, we next present a selection of model-based paradigms that integrate data acquisitions with modelling to understand, simulate and predict lung dynamics in health and disease. Finally, we highlight possible future directions where computational modelling can improve our understanding of the structure-function relationship in the lung.

Modeling Mechanical Ventilation In Silico-Potential and Pitfalls

Computer simulation offers a fresh approach to traditional medical research that is particularly well suited to investigating issues related to mechanical ventilation. Patients receiving mechanical ventilation are routinely monitored in great detail, providing extensive high-quality data-streams for model design and configuration. Models based on such data can incorporate very complex system dynamics that can be validated against patient responses for use as investigational surrogates. Crucially, simulation offers the potential to "look inside" the patient, allowing unimpeded access to all variables of interest. In contrast to trials on both animal models and human patients, in silico models are completely configurable and reproducible; for example, different ventilator settings can be applied to an identical virtual patient, or the same settings applied to different patients, to understand their mode of action and quantitatively compare their effectiveness. Here, we review progress on the mathematical modeling and computer simulation of human anatomy, physiology, and pathophysiology in the context of mechanical ventilation, with an emphasis on the clinical applications of this approach in various disease states. We present new results highlighting the link between model complexity and predictive capability, using data on the responses of individual patients with acute respiratory distress syndrome to changes in multiple ventilator settings. The current limitations and potential of in silico modeling are discussed from a clinical perspective, and future challenges and research directions highlighted.

An Optimal Electrical Impedance Tomography Drive Pattern for Human-Computer Interaction Applications

In this article, we presented an optimal Electrical Impedance Tomography (EIT) drive pattern based on feature selection and model explanation, and proposed a portable EIT system for applications in human-computer interaction for gesture recognition and contact detection, which can reduce the measurement time and realize a performance trade-off between the accuracy and the time response. In our experiment, eleven hand gestures were designed to verify the proposed approach and EIT system. Compared to the traditional eight-electrode method, the optimal electrode drive pattern achieved a recognition accuracy of 97.5% with seven electrodes and the measurement time was reduced by 60%. To illustrate the universality of this method, we performed a contact detection experiment. By setting seven labels on the conductive panel and using optimal electrode drive pattern, the detection accuracy reached 100% with seven electrodes and the measurement time was reduced by 85%.

Prediction of Aerosol Deposition in the Human Respiratory Tract via Computational Models: A Review with Recent Updates

The measurement of deposited aerosol particles in the respiratory tract via in vivo and in vitro approaches is difficult due to those approaches’ many limitations. In order to overcome these obstacles, different computational models have been developed to predict the deposition of aerosol particles inside the lung. Recently, some remarkable models have been developed based on conventional semi-empirical models, one-dimensional whole-lung models, three-dimensional computational fluid dynamics models, and artificial neural networks for the prediction of aerosol-particle deposition with a high accuracy relative to experimental data. However, these models still have some disadvantages that should be overcome shortly. In this paper, we take a closer look at the current research trends as well as the future directions of this research area.

Integrated lung tissue mechanics one piece at a time: Computational modeling across the scales of biology

The lung is a delicately balanced and highly integrated mechanical system. Lung tissue is continuously exposed to the environment via the air we breathe, making it susceptible to damage. As a consequence, respiratory diseases present a huge burden on society and their prevalence continues to rise. Emergent function is produced not only by the sum of the function of its individual components but also by the complex feedback and interactions occurring across the biological scales - from genes to proteins, cells, tissue and whole organ - and back again. Computational modeling provides the necessary framework for pulling apart and putting back together the pieces of the body and organ systems so that we can fully understand how they function in both health and disease. In this review, we discuss models of lung tissue mechanics spanning from the protein level (the extracellular matrix) through to the level of cells, tissue and whole organ, many of which have been developed in isolation. This is a vital step in the process but to understand the emergent behavior of the lung, we must work towards integrating these component parts and accounting for feedback across the scales, such as mechanotransduction. These interactions will be key to unlocking the mechanisms occurring in disease and in seeking new pharmacological targets and improving personalized healthcare.

Constituent-specific material behavior of soft biological tissue: experimental quantification and numerical identification for lung parenchyma

In this study, we present a method to experimentally quantify and numerically identify the constituent-specific material behavior of soft biological tissues. This allows the clear identification of the individual contributions of major load-bearing constituents and their interactions in the constitutive law. While the overall approach is applicable for many tissues, here it will be presented for the identification of a sophisticated constituent-specific material model of viable lung parenchyma. This material model will help to better model the effects of various lung diseases that feature altered fiber content in the lungs, such as emphysema or fibrosis. To experimentally quantify the mechanical properties of collagen, elastin, collagen-elastin-fiber interactions, and ground substance, we examined 18 collagenase and elastase treated rat lung parenchymal slices. The mechanical contributions of the collagen and elastin fibers in the living tissue were inferred from uniaxial tension tests comparing the behavior before and after the selective digestion of the respective fibers. In order to also obtain the mechanical influence of the ground substance, we consecutively treated the samples with both proteases. Collagen and elastin fibers are morphologically interconnected. Thus, a mechanical interaction between these fibers appears likely, but has not yet been experimentally verified. In this paper, we propose an experimental method to quantitatively assess the mechanical behavior of these collagen-elastin-fiber interactions. Based on our experiments, we have identified individual material models within a nonlinear continuum mechanics framework for each load-bearing component via an inverse analysis. The proposed constituent-specific material law can be incorporated into computational models of the respiratory system to simulate and even predict the behavior and alteration of the individual constituents and their effect on the whole respiratory system during normal and artificial breathing, in particular in the case of diseases that alter the fibers in the tissue.

Patient-Specific Computational Simulations of Hyperpolarized 3He MRI Ventilation Defects in Healthy and Asthmatic Subjects

Combined, medical imaging data and respiratory computer simulations may facilitate novel insight into pulmonary disease phenotypes, including the structure/function relationships within the airways. This integration may ultimately enable improved classification and treatment of asthma. Severe asthma (15% of asthmatics) is particularly challenging to treat, as these patients do not respond well to inhaled therapeutics.

METHODS

This study combines medical image data with patient-specific computational models to predict gas distributions and airway mechanics in healthy and asthmatic subjects. We achieve this by integrating segmental volume defect percent (SVDP), measured from hyperpolarized 3He MRI and CT images, to create models of patient-specific gas flow within the conducting airways. Predicted and measured SVDP distributions are achieved when the prescribed resistances are increased systematically.

RESULTS

Because of differences in airway morphology and regional function, airway resistances and flow structures varied between the asthmatic subjects. Specifically, while mean SVDP was similar between the severe asthmatics (4.30±5.22 versus 3.54±5.98%), one subject exhibited abnormal flow structures, high near wall flow gradients, and enhanced conducting airway resistances (17.3E-3versus 1.1E-3 cmH2O-s/mL) in comparison to the other severe asthmatic subject.

CONCLUSION

By coupling medical imaging data with computer simulations, we provide detailed insight into pathological flow characteristics and airway mechanics in asthmatics, beyond what could be inferred independently.

Inter-airway structural heterogeneity interacts with dynamic heterogeneity to determine lung function and flow patterns in both asthmatic and control simulated lungs

Asthma is a disease involving both airway remodelling (e.g. thickening of the airway wall) and acute, reversible airway narrowing driven by airway smooth muscle contraction. Both of these processes are known to be heterogeneous, and in this study we consider a new theoretical model which considers the interactions of both mechanisms: structural heterogeneity (variation in airway remodelling) and dynamic heterogeneity (emergent variation in airway narrowing and flow). By integrating both types of inter-airway heterogeneity in a full human lung geometry, we are able to draw several insights regarding the mechanisms underlying observed ventilation heterogeneity. We show that: (1) bimodal ventilation distributions are driven by paradoxical contraction/dilation patterns for airways of all sizes; (2) structural heterogeneity differences between asthmatic and control subjects significantly influences resulting lung function, and observed ventilation heterogeneity patterns; and (3) individual airway dilation probabilities are uncorrelated with prior airway remodelling of that airway.