A machine learning model to estimate myocardial stiffness from EDPVR

Personalized evaluation of the passive myocardium in ischemic cardiomyopathy via computational modeling using Bayesian optimization

Biomechanics-based patient-specific modeling is a promising approach that has proved invaluable for its clinical potential to assess the adversities caused by ischemic heart disease (IHD). In the present study, we propose a framework to find the passive material properties of the myocardium and the unloaded shape of cardiac ventricles simultaneously in patients diagnosed with ischemic cardiomyopathy (ICM). This was achieved by minimizing the difference between the simulated and the target end-diastolic pressure-volume relationships (EDPVRs) using black-box Bayesian optimization, based on the finite element analysis (FEA). End-diastolic (ED) biventricular geometry and the location of the ischemia were determined from cardiac magnetic resonance (CMR) imaging. We employed our pipeline to model the cardiac ventricles of three patients aged between 57 and 66 years, with and without the inclusion of valves. An excellent agreement between the simulated and the target EDPVRs has been reached. Our results revealed that the incorporation of valvular springs typically leads to lower hyperelastic parameters for both healthy and ischemic myocardium, as well as a higher fiber Green strain in the viable regions compared to models without valvular stiffness. Furthermore, the addition of valve-related effects did not result in significant changes in myofiber stress after optimization. We concluded that more accurate results could be obtained when cardiac valves were considered in modeling ventricles. The present novel and practical methodology paves the way for developing digital twins of ischemic cardiac ventricles, providing a non-invasive assessment for designing optimal personalized therapies in precision medicine.

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.

Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning

Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post-MI care. Late gadolinium enhancement-cardiac magnetic resonance (LGE-CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE-CMR is the invasive intravenous introduction of gadolinium-based contrast agents that present potential high-risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, we develop a completely non-invasive methodology that identifies the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this transformative approach, we demonstrate the remarkable performance of a multi-fidelity ML model that combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. Our results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation-based data with very small amounts of in-vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited.

Complete spatiotemporal quantification of cardiac motion in mice through enhanced acquisition and super-resolution reconstruction

The quantification of cardiac motion using cardiac magnetic resonance imaging (CMR) has shown promise as an early-stage marker for cardiovascular diseases. Despite the growing popularity of CMR-based myocardial strain calculations, measures of complete spatiotemporal strains (i.e., three-dimensional strains over the cardiac cycle) remain elusive. Complete spatiotemporal strain calculations are primarily hampered by poor spatial resolution, with the rapid motion of the cardiac wall also challenging the reproducibility of such strains. We hypothesize that a super-resolution reconstruction (SRR) framework that leverages combined image acquisitions at multiple orientations will enhance the reproducibility of complete spatiotemporal strain estimation. Two sets of CMR acquisitions were obtained for five wild-type mice, combining short-axis scans with radial and orthogonal long-axis scans. Super-resolution reconstruction, integrated with tissue classification, was performed to generate full four-dimensional (4D) images. The resulting enhanced and full 4D images enabled complete quantification of the motion in terms of 4D myocardial strains. Additionally, the effects of SRR in improving accurate strain measurements were evaluated using an in-silico heart phantom. The SRR framework revealed near isotropic spatial resolution, high structural similarity, and minimal loss of contrast, which led to overall improvements in strain accuracy. In essence, a comprehensive methodology was generated to quantify complete and reproducible myocardial deformation, aiding in the much-needed standardization of complete spatiotemporal strain calculations.

Right ventricular stiffening and anisotropy alterations in pulmonary hypertension: Mechanisms and relations to function

Aims

Pulmonary hypertension (PH) results in an increase in RV afterload, leading to RV dysfunction and failure. The mechanisms underlying maladaptive RV remodeling are poorly understood. In this study, we investigated the multiscale and mechanistic nature of RV free wall (RVFW) biomechanical remodeling and its correlations with RV function adaptations.

Methods and Results

Mild and severe models of PH, consisting of hypoxia (Hx) model in Sprague-Dawley (SD) rats (n=6 each, Control and PH) and Sugen-hypoxia (SuHx) model in Fischer (CDF) rats (n=6 each, Control and PH), were used. Organ-level function and tissue-level stiffness and microstructure were quantified through in-vivo and ex-vivo measures, respectively. Multiscale analysis was used to determine the association between fiber-level remodeling, tissue-level stiffening, and organ-level dysfunction. Animal models with different PH severity provided a wide range of RVFW stiffening and anisotropy alterations in PH. Decreased RV-pulmonary artery (PA) coupling correlated strongly with stiffening but showed a weaker association with the loss of RVFW anisotropy. Machine learning classification identified the range of adaptive and maladaptive RVFW stiffening. Multiscale modeling revealed that increased collagen fiber tautness was a key remodeling mechanism that differentiated severe from mild stiffening. Myofiber orientation analysis indicated a shift away from the predominantly circumferential fibers observed in healthy RVFW specimens, leading to a significant loss of tissue anisotropy.

Conclusion

Multiscale biomechanical analysis indicated that although hypertrophy and fibrosis occur in both mild and severe PH, certain fiber-level remodeling events, including increased tautness in the newly deposited collagen fibers and significant reorientations of myofibers, contributed to excessive biomechanical maladaptation of the RVFW leading to severe RV-PA uncoupling. Collagen fiber remodeling and the loss of tissue anisotropy can provide an improved understanding of the transition from adaptive to maladaptive remodeling.

Translational perspective

Right ventricular (RV) failure is a leading cause of mortality in patients with pulmonary hypertension (PH). RV diastolic and systolic impairments are evident in PH patients. Stiffening of the RV wall tissue and changes in the wall anisotropy are expected to be major contributors to both impairments. Global assessments of the RV function remain inadequate in identifying patients with maladaptive RV wall remodeling primarily due to their confounded and weak representation of RV fiber and tissue remodeling events. This study provides novel insights into the underlying mechanisms of RV biomechanical remodeling and identifies the adaptive-to-maladaptive transition across the RV biomechanics-function spectrum. Our analysis dissecting the contribution of different RV wall remodeling events to RV dysfunction determines the most adverse fiber-level remodeling to RV dysfunction as new therapeutic targets to curtail RV maladaptation and, in turn, RV failure in PH.

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.

An image-driven micromechanical approach to characterize multiscale remodeling in infarcted myocardium

Myocardial infarction (MI) is accompanied by the formation of a fibrotic scar in the left ventricle (LV) and initiates significant alterations in the architecture and constituents of the LV free wall (LVFW). Previous studies have shown that LV adaptation is highly individual, indicating that the identification of remodeling mechanisms post-MI demands a fully subject-specific approach that can integrate a host of structural alterations at the fiber-level to changes in bulk biomechanical adaptation at the tissue-level. We present an image-driven micromechanical approach to characterize remodeling, assimilating new biaxial mechanical data, histological studies, and digital image correlation data within an in-silico framework to elucidate the fiber-level remodeling mechanisms that drive tissue-level adaptation for each subject. We found that a progressively diffused collagen fiber structure combined with similarly disorganized myofiber architecture in the healthy region leads to the loss of LVFW anisotropy post-MI, offering an important tissue-level hallmark for LV maladaptation. In contrast, our results suggest that reductions in collagen undulation are an adaptive mechanism competing against LVFW thinning. Additionally, we show that the inclusion of subject-specific geometry when modeling myocardial tissue is essential for accurate prediction of tissue kinematics. Our approach serves as an essential step toward identifying fiber-level remodeling indices that govern the transition of MI to systolic heart failure. These indices complement the traditional, organ-level measures of LV anatomy and function that often fall short of early prognostication of heart failure in MI. In addition, our approach offers an integrated methodology to advance the design of personalized interventions, such as hydrogel injection, to reinforce and suppress native adaptive and maladaptive mechanisms, respectively, to prevent the transition of MI to heart failure. STATEMENT OF SIGNIFICANCE: Biomechanical and architectural adaptation of the LVFW remains a central, yet overlooked, remodeling process post-MI. Our study indicates the biomechanical adaptation of the LVFW post-MI is highly individual and driven by altered fiber network architecture and collective changes in collagen fiber content, undulation, and stiffness. Our findings demonstrate the possibility of using cardiac strains to infer such fiber-level remodeling events through in-silico modeling, paving the way for in-vivo characterization of multiscale biomechanical indices in humans. Such indices will complement the traditional, organ-level measures of LV anatomy and function that often fall short of early prognostication of heart failure in MI.

Machine learning-based classification of cardiac relaxation impairment using sarcomere length and intracellular calcium transients

Impaired relaxation of cardiomyocytes leads to diastolic dysfunction in the left ventricle. Relaxation velocity is regulated in part by intracellular calcium (Ca2+) cycling, and slower outflux of Ca2+ during diastole translates to reduced relaxation velocity of sarcomeres. Sarcomere length transient and intracellular calcium kinetics are integral parts of characterizing the relaxation behavior of the myocardium. However, a classifier tool that can separate normal cells from cells with impaired relaxation using sarcomere length transient and/or calcium kinetics remains to be developed. In this work, we employed nine different classifiers to classify normal and impaired cells, using ex-vivo measurements of sarcomere kinematics and intracellular calcium kinetics data. The cells were isolated from wild-type mice (referred to as normal) and transgenic mice expressing impaired left ventricular relaxation (referred to as impaired). We utilized sarcomere length transient data with a total of n = 126 cells (n = 60 normal cells and n = 66 impaired cells) and intracellular calcium cycling measurements with a total of n = 116 cells (n = 57 normal cells and n = 59 impaired cells) from normal and impaired cardiomyocytes as inputs to machine learning (ML) models for classification. We trained all ML classifiers with cross-validation method separately using both sets of input features, and compared their performance metrics. The performance of classifiers on test data showed that our soft voting classifier outperformed all other individual classifiers on both sets of input features, with 0.94 and 0.95 area under the receiver operating characteristic curves for sarcomere length transient and calcium transient, respectively, while multilayer perceptron achieved comparable scores of 0.93 and 0.95, respectively. However, the performance of decision tree, and extreme gradient boosting was found to be dependent on the set of input features used for training. Our findings highlight the importance of selecting appropriate input features and classifiers for the accurate classification of normal and impaired cells. Layer-wise relevance propagation (LRP) analysis demonstrated that the time to 50% contraction of the sarcomere had the highest relevance score for sarcomere length transient, whereas time to 50% decay of calcium had the highest relevance score for calcium transient input features. Despite the limited dataset, our study demonstrated satisfactory accuracy, suggesting that the algorithm can be used to classify relaxation behavior in cardiomyocytes when the potential relaxation impairment of the cells is unknown.

A Micro-anatomical Model of the Infarcted Left Ventricle Border Zone to Study the Influence of Collagen Undulation

Myocardial infarction (MI) results in cardiac myocyte death and often initiates the formation of a fibrotic scar in the myocardium surrounded by a border zone. Myocyte loss and collagen-rich scar tissue heavily influence the biomechanical behavior of the myocardium which could lead to various cardiac diseases such as systolic heart failure and arrhythmias. Knowledge of how myocyte and collagen micro-architecture changes affect the passive mechanical behavior of the border zone remains limited. Computational modeling provides us with an invaluable tool to identify and study the mechanisms driving the biomechanical remodeling of the myocardium post-MI. We utilized a rodent model of MI and an image-based approach to characterize the three-dimensional (3-D) myocyte and collagen micro-architecture at various timepoints post-MI. Left ventricular free wall (LVFW) samples were obtained from infarcted hearts at 1-week and 4-week post-MI (n = 1 each). Samples were labeled using immunoassays to identify the extracellular matrix (ECM) and myocytes. 3-D reconstructions of the infarct border zone were developed from confocal imaging and meshed to develop high-fidelity micro-anatomically accurate finite element models. We performed a parametric study using these models to investigate the influence of collagen undulation on the passive micromechanical behavior of the myocardium under a diastolic load. Our results suggest that although parametric increases in collagen undulation elevate the strain amount experienced by the ECM in both early- and late-stage MI, the sensitivity of myocytes to such increases is reduced from early to late-stage MI. Our 3-D micro-anatomical modeling holds promise in identifying mechanisms of border zone maladaptation post-MI.

Multiscale characterization of left ventricle active behavior in the mouse

The myocardium possesses an intricately designed microarchitecture to produce an optimal cardiac contraction. The contractile behavior of the heart is generated at the sarcomere level and travels across several length scales to manifest as the systolic function at the organ level. While passive myocardial behavior has been studied extensively, the translation of active tension produced at the fiber level to the organ-level function is not well understood. Alterations in cardiac systolic function are often key sequelae in structural heart diseases, such as myocardial infarction and systolic heart failure; thus, characterization of the contractile behavior of the heart across multiple length scales is essential to improve our understanding of mechanisms collectively leading to depressed systolic function. In this study, we present a methodology to characterize the active behavior of left ventricle free wall (LVFW) myocardial tissues in mice. Combined with active tests in papillary muscle fibers and conventional in vivo contractility measurement at the organ level in an animal-specific manner, we establish a multiscale active characterization of the heart from fiber to organ. In addition, we quantified myocardial architecture from histology to shed light on the directionality of the contractility at the tissue level. The LVFW tissue activation-relaxation behavior under isometric conditions was qualitatively similar to that of the papillary muscle fiber bundle. However, the maximum stress developed in the LVFW tissue was an order of magnitude lower than that developed by a fiber bundle, and the time taken for active forces to plateau was 2-3 orders of magnitude longer. Although the LVFW tissue exhibited a slightly stiffer passive response in the circumferential direction, the tissues produced significantly larger active stresses in the longitudinal direction during active testing. Also, contrary to passive viscoelastic stress relaxation, active stresses relaxed faster in the direction with larger peak stresses. The multiscale experimental pipeline presented in this work is expected to provide crucial insight into the contractile adaptation mechanisms of the heart with impaired systolic function. STATEMENT OF SIGNIFICANCE: Heart failure cause significant alterations to the contractile-relaxation behavior of the yocardium. Multiscale characterization of the contractile behavior of the myocardium is essential to advance our understanding of how contractility translates from fiber to organ and to identify the multiscale mechanisms leading to impaired cardiac function. While passive myocardial behavior has been studied extensively, the investigation of tissue-level contractile behavior remains critically scarce in the literature. To the best of our knowledge, our study here is the first to investigate the contractile behavior of the left ventricle at multiple length scales in small animals. Our results indicate that the active myocardial wall is a function of transmural depth and relaxes faster in the direction with larger peak stresses.

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.