Improving the Accuracy of Registration-Based Biomechanical Analysis: A Finite Element Approach to Lung Regional Strain Quantification

A machine-learning regional clustering approach to understand ventilator-induced lung injury: a proof-of-concept experimental study

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.

Physics-informed motion registration of lung parenchyma across static CT images

Lung injuries, such as ventilator-induced lung injury and radiation-induced lung injury, can lead to heterogeneous alterations in the biomechanical behavior of the lungs. While imaging methods, e.g., X-ray and static computed tomography (CT), can point to regional alterations in lung structure between healthy and diseased tissue, they fall short of delineating timewise kinematic variations between the former and the latter. Image registration has gained recent interest as a tool to estimate the displacement experienced by the lungs during respiration via regional deformation metrics such as volumetric expansion and distortion. However, successful image registration commonly relies on a temporal series of image stacks with small displacements in the lungs across succeeding image stacks, which remains limited in static imaging. In this study, we have presented a finite element (FE) method to estimate strains from static images acquired at the end-expiration (EE) and end-inspiration (EI) timepoints, i.e., images with a large deformation between the two distant timepoints. Physiologically realistic loads were applied to the geometry obtained at EE to deform this geometry to match the geometry obtained at EI. The results indicated that the simulation could minimize the error between the two geometries. Using four-dimensional (4D) dynamic CT in a rat, the strain at an isolated transverse plane estimated by our method showed sufficient agreement with that estimated through non-rigid image registration that used all the timepoints. Through the proposed method, we can estimate the lung deformation at any timepoint between EE and EI. The proposed method offers a tool to estimate timewise regional deformation in the lungs using only static images acquired at EE and EI.

Mechanical and morphological characterization of the emphysematous lung tissue

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.

In-silico CT lung phantom generated from finite-element mesh

Several lung diseases lead to alterations in regional lung mechanics, including ventilator- and radiation-induced lung injuries. Such alterations can lead to localized underventilation of the affected areas, resulting in the overdistension of the surrounding healthy regions. Thus, there has been growing interest in quantifying the dynamics of the lung parenchyma using regional biomechanical markers. Image registration through dynamic imaging has emerged as a powerful tool to assess lung parenchyma's kinematic and deformation behaviors during respiration. However, the difficulty in validating the image registration estimation of lung deformation, primarily due to the lack of ground-truth deformation data, has limited its use in clinical settings. To address this barrier, we developed a method to convert a finite-element (FE) mesh of the lung into a phantom computed tomography (CT) image, advantageously possessing ground-truth information included in the FE model. The phantom CT images generated from the FE mesh replicated the geometry of the lung and large airways that were included in the FE model. Using spatial frequency response, we investigated the effect of " imaging parameters" such as voxel size (resolution) and proximity threshold values on image quality. A series of high-quality phantom images generated from the FE model simulating the respiratory cycle will allow for the validation and evaluation of image registration-based estimations of lung deformation. In addition, the present method could be used to generate synthetic data needed to train machine-learning models to estimate kinematic biomarkers from medical images that could serve as important diagnostic tools to assess heterogeneous lung injuries.

Machine learning modeling of lung mechanics: Assessing the variability and propagation of uncertainty in respiratory-system compliance and airway resistance

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-UQ RESULTS: 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 (R2∼0.99). In terms of computational efficiency, our ML model delivers a massive speed-up of ∼970,000× 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.

Volumetric versus distortional deformation in rat lungs

There are several lung diseases that lead to alterations in regional lung mechanics, including acute respiratory distress syndrome. Such alterations can lead to localized underventilation of the affected areas resulting in the overdistension of the surrounding healthy regions. They can also lead to the surrounding alveoli expanding unevenly or distorting. Therefore, the quantification of the regional deformation in the lungs offers insights into identifying the regions at risk of lung injury. Although few recent studies have developed image processing techniques to quantify the regional volumetric deformation in the lung from dynamic imaging, the presence and extent of distortional deformation in the lung, and its correlation with volumetric deformation, remain poorly understood. In this study, we present a method that uses the four-dimensional displacement field obtained from image registration to quantify both regional volumetric and distortional deformation in the lung. We used dynamic computed tomography scans in a healthy rat over the course of one respiratory cycle in free breathing. Non-rigid image registration was performed to quantify voxel displacement during respiration. The deformation gradient was calculated using the displacement field and its determinant was used to quantify regional volumetric deformation. Regional distortion was calculated as the ratio of maximum to minimum principal stretches using the isochoric part of the Cauchy green tensor. We found an inverse correlation between volumetric strains and distortion indicating that poorly expanding alveoli tend to experience larger distortion. The combination of regional volumetric strains and distortion may serve as high-fidelity biomarkers to identify the regions at risk of most adverse lung injuries.

Lung Mechanics: A Review of Solid Mechanical Elasticity in Lung Parenchyma

The lung is the main organ of the respiratory system. Its purpose is to facilitate gas exchange (breathing). Mechanically, breathing may be described as the cyclic application of stresses acting upon the lung surface. These forces are offset by prominent stress-bearing components of lung tissue. These components result from the mechanical elastic properties of lung parenchyma. Various studies have been dedicated to understanding the macroscopic behaviour of parenchyma. This has been achieved through pressure-volume analysis, numerical methods, the development of constitutive equations or strain-energy functions, finite element methods, image processing and elastography. Constitutive equations can describe the elastic behaviour exhibited by lung parenchyma through the relationship between the macroscopic stress and strain. The research conducted within lung mechanics around the elastic and resistive properties of the lung has allowed scientists to develop new methods and equipment for evaluating and treating pulmonary pathogens. This paper establishes a review of mathematical studies conducted within lung mechanics, centering on the development and implementation of solid mechanics to the understanding of the mechanical properties of the lung. Under the classical theory of elasticity, the lung is said to behave as an isotropic elastic continuum undergoing small deformations. However, the lung has also been known to display heterogeneous anisotropic behaviour associated with large deformations. Therefore, focus is placed on the assumptions and development of the various models, their mechanical influence on lung physiology, and the development of constitutive equations through the classical and non-classical theory of elasticity. Lastly, we also look at lung blast mechanics. No explicit emphasis is placed on lung pathology.

Whole-lung finite-element models for mechanical ventilation and respiratory research applications

Mechanical ventilation has been a vital treatment for Covid-19 patients with respiratory failure. Lungs assisted with mechanical ventilators present a wide variability in their response that strongly depends on air-tissue interactions, which motivates the creation of simulation tools to enhance the design of ventilatory protocols. In this work, we aim to create anatomical computational models of the lungs that predict clinically-relevant respiratory variables. To this end, we formulate a continuum poromechanical framework that seamlessly accounts for the air-tissue interaction in the lung parenchyma. Based on this formulation, we construct anatomical finite-element models of the human lungs from computed-tomography images. We simulate the 3D response of lungs connected to mechanical ventilation, from which we recover physiological parameters of high clinical relevance. In particular, we provide a framework to estimate respiratory-system compliance and resistance from continuum lung dynamic simulations. We further study our computational framework in the simulation of the supersyringe method to construct pressure-volume curves. In addition, we run these simulations using several state-of-the-art lung tissue models to understand how the choice of constitutive models impacts the whole-organ mechanical response. We show that the proposed lung model predicts physiological variables, such as airway pressure, flow and volume, that capture many distinctive features observed in mechanical ventilation and the supersyringe method. We further conclude that some constitutive lung tissue models may not adequately capture the physiological behavior of lungs, as measured in terms of lung respiratory-system compliance. Our findings constitute a proof of concept that finite-element poromechanical models of the lungs can be predictive of clinically-relevant variables in respiratory medicine.

Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image

Most of the people all over the world pass away from complications related to lung cancer every single day. It is a deadly form of the disease. To improve a person's chances of survival, an early diagnosis is a necessary prerequisite. In this regard, the existing methods of tumour detection, such as CT scans, are most commonly used to recognize infected regions. Despite this, there are certain obstacles presented by CT imaging, so this paper proposes a novel model which is a correlation-based model designed for analysis of lung cancer. When registering pictures of thoracic and abdominal organs with slider motion, the total variation regularization term may correct the border discontinuous displacement field, but it cannot maintain the local characteristics of the image and loses the registration accuracy. The thin-plate spline energy operator and the total variation operator are spatially weighted via the spatial position weight of the pixel points to construct an adaptive thin-plate spline total variation regular term for lung image CT single-mode registration and CT/PET dual-mode registration. The regular term is then combined with the CRMI similarity measure and the L-BFGS optimization approach to create a nonrigid registration procedure. The proposed method assures the smoothness of interior of the picture while ensuring the discontinuous motion of the border and has greater registration accuracy, according to the experimental findings on the DIR-Lab 4D-CT public dataset and the CT/PET clinical dataset.

Estimation of Regional Pulmonary Compliance in Idiopathic Pulmonary Fibrosis Based On Personalized Lung Poromechanical Modeling

Pulmonary function is tightly linked to the lung mechanical behavior, especially large deformation during breathing. Interstitial lung diseases, such as Idiopathic Pulmonary Fibrosis (IPF), have an impact on the pulmonary mechanics and consequently alter lung function. However, IPF remains poorly understood, poorly diagnosed and poorly treated. Currently, the mechanical impact of such diseases is assessed by pressure-volume curves, giving only global information. We developed a poromechanical model of the lung that can be personalized to a patient based on routine clinical data. The personalization pipeline uses clinical data, mainly CT-images at two time steps and involves the formulation of an inverse problem to estimate regional compliances. The estimation problem can be formulated both in terms of "effective", i.e., without considering the mixture porosity, or "rescaled", i.e., where the first-order effect of the porosity has been taken into account, compliances. Regional compliances are estimated for one control subject and three IPF patients, allowing to quantify the IPF-induced tissue stiffening. This personalized model could be used in the clinic as an objective and quantitative tool for IPF diagnosis.

Distribution and Magnitude of Regional Volumetric Lung Strain and Its Modification by PEEP in Healthy Anesthetized and Mechanically Ventilated Dogs

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 (LS_TOTAL) has a dynamic (LS_DYNAMIC) and a static (LS_STATIC) component. Due to lung heterogeneity, global lung strain may not accurately represent regional total tissue lung strain (TS_TOTAL), which may also be described by a regional dynamic (TS_DYNAMIC) and static (TS_STATIC) 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 LS_TOTAL, LS_DYNAMIC, and LS_STATIC during PEEP. During ZEEP, LS_STATIC was assumed zero; therefore, LS_TOTAL was the same as LS_DYNAMIC. Image segmentation was applied to CT images to obtain maps of regional TS_TOTAL, TS_DYNAMIC, and TS_STATIC during PEEP, and TS_DYNAMIC 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 LS_DYNAMIC (33.4 ± 6.4% during ZEEP, 24.0 ± 4.6% during PEEP, p = 0.032). LS_STATIC by PEEP was larger than the reduction in LS_DYNAMIC; therefore, LS_TOTAL at PEEP was larger than LS_DYNAMIC at ZEEP (p = 0.005). There was marked topographic heterogeneity of regional strains. PEEP induced a significant reduction in TS_DYNAMIC in all lung regions (p < 0.05). Similar to global findings, PEEP-induced TS_STATIC was larger than the reduction in TS_DYNAMIC; therefore, PEEP-induced TS_TOTAL was larger than TS_DYNAMIC 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.

Evaluation of Lung Aeration and Respiratory System Mechanics in Obese Dogs Ventilated With Tidal Volumes Based on Ideal vs. Current Body Weight

We describe the respiratory mechanics and lung aeration in anesthetized obese dogs ventilated with tidal volumes (VT) based on ideal (VTi) vs. current (VTc) body weight. Six dogs with body condition scores ≥ 8/9 were included. End-expiratory respiratory mechanics and end-expiratory CT-scan were obtained at baseline for each dog. Thereafter, dogs were ventilated with VT 15 ml kg⁻¹ based on VTi and VTc, applied randomly. Respiratory mechanics and CT-scan were repeated at end-inspiration during VTi and VTc. Data analyzed with linear mixed models and reported as mean ± SD or median [range]. Statistical significance p < 0.05. The elastance of the lung, chest wall and respiratory system indexed by ideal body weight (IBW) were positively correlated with body fat percentage, whereas the functional residual capacity indexed by IBW was negatively correlated with body fat percentage. At end-expiration, aeration (%) was: hyperaeration 0.03 [0.00–3.35], normoaeration 69.7 [44.6–82.2], hypoaeration 29.3 [13.6–49.4] and nonaeration (1.06% [0.37–6.02]). Next to the diaphragm, normoaeration dropped to 12 ± 11% and hypoaeration increased to 90 ± 8%. No differences in aeration between groups were found at end-inspiration. Airway driving pressure (cm H₂O) was higher (p = 0.002) during VTc (9.8 ± 0.7) compared with VTi (7.6 ± 0.4). Lung strain was higher (p = 0.014) during VTc (55 ± 21%) than VTi (38 ± 10%). The stress index was higher (p = 0.012) during VTc (SI = 1.07 [0.14]) compared with VTi (SI = 0.93 [0.18]). This study indicates that body fat percentage influences the magnitude of lung, chest wall, and total respiratory system elastance and resistance, as well as functional residual capacity. Further, these results indicate that obese dogs have extensive areas of hypoaerated lungs, especially in caudodorsal regions. Finally, lung strain and airway driving pressure, surrogates of lung deformation, are higher during VTc than during VTi, suggesting that in obese anesthetized dogs, ventilation protocols based on IBW may be advantageous.

Estimation of changes in cyclic lung strain by electrical impedance tomography: Proof-of-concept study

RATIONALE

Cyclic strain may be a determinant of ventilator-induced lung injury. The standard for strain assessment is the computed tomography (CT), which does not allow continuous monitoring and exposes to radiation. Electrical impedance tomography (EIT) is able to monitor changes in regional lung ventilation. In addition, there is a correlation between mechanical deformation of materials and detectable changes in its electrical impedance, making EIT a potential surrogate for cyclic lung strain measured by CT (Strain_CT ).

OBJECTIVES

To compare the global Strain_CT with the change in electrical impedance (ΔZ).

METHODS

Acute respiratory distress syndrome patients under mechanical ventilation (V_T 6 mL/kg ideal body weight with positive end-expiratory pressure 5 [PEEP 5] and best PEEP according to EIT) underwent whole-lung CT at end-inspiration and end-expiration. Biomechanical analysis was used to construct 3D maps and determine Strain_CT at different levels of PEEP. CT and EIT acquisitions were performed simultaneously. Multilevel analysis was employed to determine the causal association between Strain_CT and ΔZ. Linear regression models were used to predict the change in lung Strain_CT between different PEEP levels based on the change in ΔZ.

MAIN RESULTS

Strain_CT was positively and independently associated with ΔZ at global level (P < .01). Furthermore, the change in Strain_CT (between PEEP 5 and Best PEEP) was accurately predicted by the change in ΔZ (R² 0.855, P < .001 at global level) with a high agreement between predicted and measured Strain_CT .

CONCLUSIONS

The change in electrical impedance may provide a noninvasive assessment of global cyclic strain, without radiation at bedside.

Effect of pedicle screw angles on the fracture risk of the human vertebra: A patient-specific computational model

The assessment of a human vertebra's stability after a screws fixation procedure and its fracture risk is still an open clinical problem. The accurate evaluation of fracture risk requires that all fracture mechanical determinants such as geometry, constitutive behavior, loading modes, and screws angulation are accounted for, which requires biomechanics-based analyses. As such, in the present work we investigate the effect of pedicle screws angulation on a patient-specific model of non osteoporotic lumbar vertebra, derived from clinical CT images. We propose a novel computational approach of fracture analysis and compare the effects of fixation stability in the lumbar spine. We considered a CT-based three-dimensional FE model of bilaterally instrumented L4 vertebra virtually implanting pedicle screws according to clinical guidelines. Nine screws trajectories were selected combining three craniocaudal and mediolateral angles, thus investigated through a parametric computational analysis. Bone was modeled as an elastic material with element-wise inhomogeneous properties fine-tuned on CT data. We implemented a custom algorithm to identify the thin cortical layer correctly from CT images ensuring reliable material properties in the computational model. Physiological motion (i.e., flexion, extension, axial rotation, lateral bending) was further accomplished by simultaneously loading the vertebra and the implant. We simulated local progressive damage of the bone by using a quasi-static force-driven incremental approach and considering a stress-based fracture criterion. Ductile-like and brittle-like fractures were found. Statistical analyses show significant differences comparing screws trajectories and averaging the results among six loading modes. In particular, we identified the caudomedial trajectory as the least critical case, thus safer from a clinical perspective. Instead, medial and craniolaterally oriented screws entailed higher peak and average stresses, though no statistical evidence classified such loads as the most critical scenarios. A quantitative validation procedure will be required in the future to translate our findings into clinical practice. Besides, to apply the results to the target osteoporotic population, new studies will be needed, including a specimen from an osteoporotic patient and the effect of osteoporosis on the constitutive model of bone.

Correlating Local Volumetric Tissue Strains with Global Lung Mechanics Measurements

The mechanics of breathing is a fascinating and vital process. The lung has complexities and subtle heterogeneities in structure across length scales that influence mechanics and function. This study establishes an experimental pipeline for capturing alveolar deformations during a respiratory cycle using synchrotron radiation micro-computed tomography (SR-micro-CT). Rodent lungs were mechanically ventilated and imaged at various time points during the respiratory cycle. Pressure-Volume (P-V) characteristics were recorded to capture any changes in overall lung mechanical behaviour during the experiment. A sequence of tomograms was collected from the lungs within the intact thoracic cavity. Digital volume correlation (DVC) was used to compute the three-dimensional strain field at the alveolar level from the time sequence of reconstructed tomograms. Regional differences in ventilation were highlighted during the respiratory cycle, relating the local strains within the lung tissue to the global ventilation measurements. Strains locally reached approximately 150% compared to the averaged regional deformations of approximately 80-100%. Redistribution of air within the lungs was observed during cycling. Regions which were relatively poorly ventilated (low deformations compared to its neighbouring region) were deforming more uniformly at later stages of the experiment (consistent with its neighbouring region). Such heterogenous phenomena are common in everyday breathing. In pathological lungs, some of these non-uniformities in deformation behaviour can become exaggerated, leading to poor function or further damage. The technique presented can help characterize the multiscale biomechanical nature of a given pathology to improve patient management strategies, considering both the local and global lung mechanics.

A physiological approach to understand the role of respiratory effort in the progression of lung injury in SARS-CoV-2 infection

Deterioration of lung function during the first week of COVID-19 has been observed when patients remain with insufficient respiratory support. Patient self-inflicted lung injury (P-SILI) is theorized as the responsible, but there is not robust experimental and clinical data to support it. Given the limited understanding of P-SILI, we describe the physiological basis of P-SILI and we show experimental data to comprehend the role of regional strain and heterogeneity in lung injury due to increased work of breathing.

In addition, we discuss the current approach to respiratory support for COVID-19 under this point of view.

Progression of regional lung strain and heterogeneity in lung injury: assessing the evolution under spontaneous breathing and mechanical ventilation

BACKGROUND

Protective mechanical ventilation (MV) aims at limiting global lung deformation and has been associated with better clinical outcomes in acute respiratory distress syndrome (ARDS) patients. In ARDS lungs without MV support, the mechanisms and evolution of lung tissue deformation remain understudied. In this work, we quantify the progression and heterogeneity of regional strain in injured lungs under spontaneous breathing and under MV.

METHODS

Lung injury was induced by lung lavage in murine subjects, followed by 3 h of spontaneous breathing (SB-group) or 3 h of low V_t mechanical ventilation (MV-group). Micro-CT images were acquired in all subjects at the beginning and at the end of the ventilation stage following induction of lung injury. Regional strain, strain progression and strain heterogeneity were computed from image-based biomechanical analysis. Three-dimensional regional strain maps were constructed, from which a region-of-interest (ROI) analysis was performed for the regional strain, the strain progression, and the strain heterogeneity.

RESULTS

After 3 h of ventilation, regional strain levels were significantly higher in 43.7% of the ROIs in the SB-group. Significant increase in regional strain was found in 1.2% of the ROIs in the MV-group. Progression of regional strain was found in 100% of the ROIs in the SB-group, whereas the MV-group displayed strain progression in 1.2% of the ROIs. Progression in regional strain heterogeneity was found in 23.4% of the ROIs in the SB-group, while the MV-group resulted in 4.7% of the ROIs showing significant changes. Deformation progression is concurrent with an increase of non-aerated compartment in SB-group (from 13.3% ± 1.6% to 37.5% ± 3.1%), being higher in ventral regions of the lung.

CONCLUSIONS

Spontaneous breathing in lung injury promotes regional strain and strain heterogeneity progression. In contrast, low V_t MV prevents regional strain and heterogeneity progression in injured lungs.

3D printed deformable sensors

The ability to directly print compliant biomedical devices on live human organs could benefit patient monitoring and wound treatment, which requires the 3D printer to adapt to the various deformations of the biological surface. We developed an in situ 3D printing system that estimates the motion and deformation of the target surface to adapt the toolpath in real time. With this printing system, a hydrogel-based sensor was printed on a porcine lung under respiration-induced deformation. The sensor was compliant to the tissue surface and provided continuous spatial mapping of deformation via electrical impedance tomography. This adaptive 3D printing approach may enhance robot-assisted medical treatments with additive manufacturing capabilities, enabling autonomous and direct printing of wearable electronics and biological materials on and inside the human body.

Dynamic single-slice CT estimates whole-lung dual-energy CT variables in pigs with and without experimental lung injury

Background

Dynamic single-slice CT (dCT) is increasingly used to examine the intra-tidal, physiological variation in aeration and lung density in experimental lung injury. The ability of dCT to predict whole-lung values is unclear, especially for dual-energy CT (DECT) variables. Additionally, the effect of inspiration-related lung movement on CT variables has not yet been quantified.

Methods

Eight domestic pigs were studied under general anaesthesia, including four following saline-lavage surfactant depletion (lung injury model). DECT, dCT and whole-lung images were collected at 12 ventilatory settings. Whole-lung single energy scans images were collected during expiratory and inspiratory apnoeas at positive end-expiratory pressures from 0 to 20 cmH₂O. Means and distributions of CT variables were calculated for both dCT and whole-lung images. The cranio-caudal displacement of the anatomical slice was measured from whole-lung images.

Results

Mean CT density and volume fractions of soft tissue, gas, iodinated blood, atelectasis, poor aeration, normal aeration and overdistension correlated between dCT and the whole lung (r² 0.75–0.94) with agreement between CT density distributions (r 0.89–0.97). Inspiration increased the matching between dCT and whole-lung values and was associated with a movement of 32% (SD 15%) of the imaged slice out of the scanner field-of-view. This effect introduced an artefactual increase in dCT mean CT density during inspiration, opposite to that caused by the underlying physiology.

Conclusions

Overall, dCT closely approximates whole-lung aeration and density. This approximation is improved by inspiration where a decrease in CT density and atelectasis can be interpreted as physiological rather than artefactual.

Mapping regional strain in anesthetised healthy subjects during spontaneous ventilation

Introduction

Breathing produces a phenomenon of cyclic deformation throughout life. Biomechanically, deformation of the lung is measured as strain. Regional strain recently started to be recognised as a tool in the study of lung pathophysiology, but regional lung strain has not been studied in healthy subjects breathing spontaneously without voluntary or pharmacological control of ventilation. Our aim is to generate three-dimensional (3D) regional strain and heterogeneity maps of healthy rat lungs and describe their changes over time.

Methods

Micro-CT and image-based biomechanical analysis by finite element approach were carried out in six anaesthetised rats under spontaneous breathing in two different states, at the beginning of the experiment and after 3 hours of observation. 3D regional strain maps were constructed and divided into 10 isovolumetric region-of-interest (ROI) in three directions (apex to base, dorsal to ventral and costal to mediastinal), allowing to regionally analyse the volumetric strain, the strain progression and the strain heterogeneity. To describe in depth these parameters, and systematise their report, we defined regional strain heterogeneity index [1+strain SD ROI(x)]/[1+strain mean ROI(x)] and regional strain progression index [ROI(x)-mean of final strain/ROI(x)-mean of initial strain].

Results

We were able to generate 3D regional strain maps of the lung in subjects without respiratory support, showing significant differences among the three analysed axes. We observed a significantly lower regional volumetric strain in the apex sector compared with the base, with no significant anatomical systematic differences in the other directions. This heterogeneity could not be identified with physiological or standard CT methods. There was no progression of the analysed regional volumetric strain when the two time-points were compared.

Discussion

It is possible to map the regional volumetric strain in the lung for healthy subjects during spontaneous breathing. Regional strain heterogeneity and changes over time can be measured using a CT image-based numerical analysis applying a finite element approach. These results support that healthy lung might have significant regional strain and its spatial distribution is highly heterogeneous. This protocol for CT image acquisition and analysis could be a useful tool for helping to understand the mechanobiology of the lung in many diseases.

Does Regional Lung Strain Correlate With Regional Inflammation in Acute Respiratory Distress Syndrome During Nonprotective Ventilation? An Experimental Porcine Study

OBJECTIVE

It is known that ventilator-induced lung injury causes increased pulmonary inflammation. It has been suggested that one of the underlying mechanisms may be strain. The aim of this study was to investigate whether lung regional strain correlates with regional inflammation in a porcine model of acute respiratory distress syndrome.

DESIGN

Retrospective analysis of CT images and positron emission tomography images using [F]fluoro-2-deoxy-D-glucose.

SETTING

University animal research laboratory.

SUBJECTS

Seven piglets subjected to experimental acute respiratory distress syndrome and five ventilated controls.

INTERVENTIONS

Acute respiratory distress syndrome was induced by repeated lung lavages, followed by 210 minutes of injurious mechanical ventilation using low positive end-expiratory pressures (mean, 4 cm H2O) and high inspiratory pressures (mean plateau pressure, 45 cm H2O). All animals were subsequently studied with CT scans acquired at end-expiration and end-inspiration, to obtain maps of volumetric strain (inspiratory volume - expiratory volume)/expiratory volume, and dynamic positron emission tomography imaging. Strain maps and positron emission tomography images were divided into 10 isogravitational horizontal regions-of-interest, from which spatial correlation was calculated for each animal.

MEASUREMENTS AND MAIN RESULTS

The acute respiratory distress syndrome model resulted in a decrease in respiratory system compliance (20.3 ± 3.4 to 14.0 ± 4.9 mL/cm H2O; p < 0.05) and oxygenation (PaO2/FIO2, 489 ± 80 to 92 ± 59; p < 0.05), whereas the control animals did not exhibit changes. In the acute respiratory distress syndrome group, strain maps showed a heterogeneous distribution with a greater concentration in the intermediate gravitational regions, which was similar to the distribution of [F]fluoro-2-deoxy-D-glucose uptake observed in the positron emission tomography images, resulting in a positive spatial correlation between both variables (median R = 0.71 [0.02-0.84]; p < 0.05 in five of seven animals), which was not observed in the control animals.

CONCLUSION

In this porcine acute respiratory distress syndrome model, regional lung strain was spatially correlated with regional inflammation, supporting that strain is a relevant and prominent determinant of ventilator-induced lung injury.

The role of three-dimensionality and alveolar pressure in the distribution and amplification of alveolar stresses

Alveolar stresses are fundamental to enable the respiration process in mammalians and have recently gained increasing attention due to their mechanobiological role in the pathogenesis and development of respiratory diseases. Despite the fundamental physiological role of stresses in the alveolar wall, the determination of alveolar stresses remains challenging, and our current knowledge is largely drawn from 2D studies that idealize the alveolar septal wall as a spring or a planar continuum. Here we study the 3D stress distribution in alveolar walls of normal lungs by combining ex-vivo micro-computed tomography and 3D finite-element analysis. Our results show that alveolar walls are subject to a fully 3D state of stresses rather than to a pure axial stress state. To understand the contributions of the different components and deformation modes, we decompose the stress tensor field into hydrostatic and deviatoric components, which are associated with isotropic and distortional stresses, respectively. Stress concentrations arise in localized regions of the alveolar microstructure, with magnitudes that can be up to 27 times the applied alveolar pressure. Interestingly, we show that the stress amplification factor strongly depends on the level of alveolar pressure, i.e, stresses do not scale proportional to the applied alveolar pressure. In addition, we show that 2D techniques to assess alveolar stresses consistently overestimate the stress magnitude in alveolar walls, particularly for lungs under high transpulmonary pressure. These findings take particular relevance in the study of stress-induced remodeling of the emphysematous lung and in ventilator-induced lung injury, where the relation between transpulmonary pressure and alveolar wall stress is key to understand mechanotransduction processes in pneumocytes.

Electrical impedance tomography in acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) is a clinical entity that acutely affects the lung parenchyma, and is characterized by diffuse alveolar damage and increased pulmonary vascular permeability. Currently, computed tomography (CT) is commonly used for classifying and prognosticating ARDS. However, performing this examination in critically ill patients is complex, due to the need to transfer these patients to the CT room. Fortunately, new technologies have been developed that allow the monitoring of patients at the bedside. Electrical impedance tomography (EIT) is a monitoring tool that allows one to evaluate at the bedside the distribution of pulmonary ventilation continuously, in real time, and which has proven to be useful in optimizing mechanical ventilation parameters in critically ill patients. Several clinical applications of EIT have been developed during the last years and the technique has been generating increasing interest among researchers. However, among clinicians, there is still a lack of knowledge regarding the technical principles of EIT and potential applications in ARDS patients. The aim of this review is to present the characteristics, technical concepts, and clinical applications of EIT, which may allow better monitoring of lung function during ARDS.

Viscoelastic computational modeling of the human head-neck system: Eigenfrequencies and time-dependent analysis

A subject-specific 3-dimensional viscoelastic finite element model of the human head-neck system is presented and investigated based on computed tomography and magnetic resonance biomedical images. Ad hoc imaging processing tools are developed for the reconstruction of the simulation domain geometry and the internal distribution of bone and soft tissues. Material viscoelastic properties are characterized point-wise through an image-based interpolating function used then for assigning the constitutive prescriptions of a heterogenous viscoelastic continuum model. The numerical study is conducted both for modal and time-dependent analyses, compared with similar studies and validated against experimental evidences. Spatiotemporal analyses are performed upon different exponential swept-sine wave-localized stimulations. The modeling approach proposes a generalized, patient-specific investigation of sound wave transmission and attenuation within the human head-neck system comprising skull and brain tissues. Model extensions and applications are finally discussed.

3D Quantification of Wall Shear Stress and Oscillatory Shear Index Using a Finite-Element Method in 3D CINE PC-MRI Data of the Thoracic Aorta

Several 2D methods have been proposed to estimate WSS and OSI from PC-MRI, neglecting the longitudinal velocity gradients that typically arise in cardiovascular flow, particularly on vessel geometries whose cross section and centerline orientation strongly vary in the axial direction. Thus, the contribution of longitudinal velocity gradients remains understudied. In this work, we propose a 3D finite-element method for the quantification of WSS and OSI from 3D-CINE PC-MRI that accounts for both in-plane and longitudinal velocity gradients. We demonstrate the convergence and robustness of the method on cylindrical geometries using a synthetic phantom based on the Poiseuille flow equation. We also show that, in the presence of noise, the method is both stable and accurate. Using computational fluid dynamics simulations, we show that the proposed 3D method results in more accurate WSS estimates than those obtained from a 2D analysis not considering out-of-plane velocity gradients. Further, we conclude that for irregular geometries the accurate prediction of WSS requires the consideration of longitudinal gradients in the velocity field. Additionally, we compute 3D maps of WSS and OSI for 3D-CINE PC-MRI data sets from an aortic phantom and sixteen healthy volunteers and two patients. The OSI values show a greater dispersion than WSS, which is strongly dependent on the PC-MRI resolution. We envision that the proposed 3D method will improve the estimation of WSS and OSI from 3D-CINE PC-MRI images, allowing for more accurate estimates in vessels with pathologies that induce high longitudinal velocity gradients, such as coarctations and aneurisms.