Purpose To generate high-resolution fibrosis maps using universal ventricular coordinates for spatial characterization of fibrotic patterns across disease severity in desmoplakin cardiomyopathy. Materials and Methods This retrospective study included patients with desmoplakin cardiomyopathy who underwent cardiac MRI between March 2012 and October 2024. Three-dimensional ventricular models were reconstructed from cine MRI acquisitions. Fibrosis identified at late gadolinium enhancement MRI was mapped to ventricular geometry using universal ventricular coordinates, enabling analysis within a common reference framework. Patients were classified as having mild, moderate, or severe disease based on fibrosis extent. Associations between fibrosis burden and left ventricular structural and functional metrics were evaluated using Tukey and Wald tests. Results Twenty-nine patients (mean age, 37.39 years [range, 10-77 years]; 20 female patients) were included. In mild disease, fibrosis was primarily located in the subepicardial midinferior region. With increasing fibrosis burden, moderate and severe disease demonstrated subepicardial circumferential involvement with a ringlike pattern in severe cases. Fibrosis burden correlated negatively with left ventricular ejection fraction (r = -0.80, P < .001) and positively with left ventricular end-diastolic volume index (r = 0.54, P = .002). Group comparisons showed significant differences between moderate and severe disease groups across all metrics (P < .05) but not between mild and moderate groups. Conclusion Use of universal ventricular coordinates enabled high-resolution mapping of fibrosis and demonstrated characteristic spatial patterns across disease severity in desmoplakin cardiomyopathy. Fibrosis was observed in nondilated ventricles, whereas higher fibrosis burden was associated with left ventricular dilatation and impaired systolic function. Keywords: Cardiomyopathies, Left Ventricle, Computer Applications-3D, MR Imaging Supplemental material is available for this article. © RSNA, 2026.

Heart disease remains the leading cause of death in the United States, motivating extensive efforts to improve its diagnosis, treatment, and prevention. Over the past decade, computational modeling has emerged as a powerful tool to advance cardiovascular research by enabling detailed, patient-specific studies of cardiac physiology and pathology. svMultiPhysics is an open-source, parallel finite element solver written in C++ specifically designed for multiphysics cardiovascular problems. It provides a unified framework for simulating the partial differential equations that govern solid mechanics, fluid dynamics, diffusion, and cardiac electrophysiology. These equations can be solved independently or in a coupled fashion, allowing researchers to investigate interactions between physical processes in a modular yet integrated way. The solver's main strength lies in its ability to seamlessly couple multiple physics modules, enabling the study of complex, highly nonlinear systems. For example, svMultiPhysics can capture the interplay between cardiac electrophysiology, myocardial tissue mechanics, and blood flow dynamics, processes that are essential to understanding vascular and cardiac physiology and function in health and disease. Preliminary GPU-enabled simulations show up to approximately 30× wall-clock speedup for selected linear solver configurations over CPU-based simulations. By offering a robust, extensible, and freely available platform, svMultiPhysics empowers researchers to explore multiphysics problems in cardiovascular science. As the primary 3D solver in the SimVascular open source project, it forms a key component of an end-to-end open source software ecosystem for image based patient specific modeling in the cardiovascular system. It is maintained and openly developed on GitHub, fostering transparency, reproducibility, and collaboration.

Engineered heart tissues (EHTs) provide a controlled platform for studying cardiac tissue mechanics under both healthy and diseased conditions. The use of imaging techniques enables the detailed characterization of tissue structure and function, allowing for the measurement of EHT changes in response to diverse stimuli. Increasingly, computational models are being built and used to help synthesize and contextualize this detailed information to understand the mechanobiology of EHTs. Many of these approaches consider a constrained mixture approach, which homogenizes the kinematic response of EHTs based on a density-weighted sum of the strain energies of the different mechanical constituents. However, the challenge when considering the mechanobiology of EHTs is whether this assumption of shared kinematics holds and its influence on the predicted mechanical response of EHTs. To explore this phenomenon, we extended our EHT modeling framework (based on constrained mixture) to consider EHTs as an UNConstrained Mixture (UN-CM), where the kinematics of fibers and cells are given by separate independent variables which are constrained at explicit cell-matrix adhesions. Predictions from both frameworks were evaluated across a range of idealized and tissue-specific models, with variations in averaged regional mechanical quantities (strain, strain rate, and stress) ranging up to 40% depending on the regions, assumptions about adhesions, and the conditions of the tests. Consistently, strain rate between models showed the greatest variance across all tests considered. These results highlight the benefits of the CM approach, the flexibility of the UN-CM approach, and the divergence of both approaches when considering local mechanics in EHTs. Statement of Significance Biomechanical computational models can augment observations from engineered heart tissue (EHT) platforms. Most existing models rely on a constrained mixture (CM) framework, in which myofibrils and extracellular matrix fibers are assumed to deform together. However, microscale experiments show that fibers can deform independently of cells, except at discrete adhesion sites. To capture this behavior, we developed an unconstrained mixture (UN-CM) framework and compared it with the CM approach across a series of test cases. Although both models produce similar predictions on average, we identified localized differences, with strain rate emerging as the most divergent variable. These findings help contextualize results from computational EHT models and provide a new framework for studying diseases that impair the cell's ability to form adhesions.

Heart failure remains a leading cause of death worldwide, largely due to the myocardium's limited regenerative capacity. While the cardiac tissue engineering field aims to improve myocardial function following injury by implanting a cardiac graft, replicating the aligned architecture of the myocardium remains a significant challenge. This study presents an engineered platform for fabricating cardiac myobundles using a natural hydrogel composite containing synthetic, cell-adhesive electrospun fibers to study the individual and synergistic roles of fiber reinforcement and cardiac fibroblasts. Our results demonstrate that cell-adhesive fibers are essential for promoting cardiomyocyte (CM) spreading and myofibril assembly. Notably, induced pluripotent stem cell-derived cardiac fibroblasts (iCFs) significantly outperformed primary cardiac fibroblasts (pCFs) in promoting tissue compaction via increased fibrinolytic activity, which was consistent across three distinct iPSC donor lines. Further, the combination of iCFs and cell-adhesive fibers resulted in myobundles with enhanced CM spread area, myofibril formation, and contraction synchronicity; these results were also observed when extended to larger tissue grafts. Overall, this work identifies cell-adhesive fibers as critical drivers of CM spreading, myofibril assembly, and CM contractile synchronicity, while determining the role of iCF-mediated tissue compaction, collectively offering an improved method for developing large-scale, functional myocardial grafts for the treatment of heart failure.

Brightfield time-lapse imaging is widely used in cardiac tissue engineering, yet the absence of standardized, interpretable analytical frameworks limits reproducibility and cross-platform comparison. We present an open, scalable computational pipeline for quantifying spatiotemporal contractile dynamics in microscopy videos of human induced pluripotent stem cell-derived cardiac microbundles. Building on our open-source tools "MicroBundleCompute" and "MicroBundlePillarTrack," we define a suite of 16 interpretable structural, functional, and spatiotemporal metrics that capture tissue deformation, synchrony, and heterogeneity. The framework integrates full-field displacement tracking, strain reconstruction, spatial registration, dimensionality reduction, and topology-based vector-field analysis within a unified workflow. Applied to a dataset of 670 cardiac microbundles spanning 20 experimental conditions, the pipeline reveals continuous variation in contractile phenotypes rather than discrete condition-specific clustering, with intra-condition variability often exceeding inter-condition differences. Redundancy analysis identifies a reduced core set of 10 metrics that retain most informational content while minimizing multicollinearity. Analysis of denoised displacement fields shows that contraction is dominated by a global isotropic mode, with localized saddle-type deformation patterns present in approximately half of the samples. All software and workflows are released openly to enable reproducible, scalable analysis of dynamic tissue mechanics.

Determining the unloaded, or reference, configuration of the heart is essential for developing patient-specific models that can accurately estimate in-vivo strains and stresses. Various strategies have been proposed to obtain this configuration, with inverse mechanics being a practical approach. The inverse mechanics method estimates the unloaded geometry from a deformed state and its known loads without the need for optimization procedures. However, for the resulting unloaded geometry to be physiologically meaningful, accurate boundary conditions are crucial. While spring boundary conditions are commonly applied to the epicardium to model interactions with surroundings, they do not account for the localized forces exerted by structures like the ribs and diaphragm. Since these external forces cannot be directly measured from medical images, we propose a novel approach that integrates them into the inverse mechanics formulation by penalizing large deformations. Using a series of test problems, we show this approach produces a reference configuration that closely matches the ground truth and improves circumferential strain estimations by an order of magnitude compared to standard inverse mechanics methods.

Developing engineered tissue patches from induced pluripotent stem cells that can be incorporated into the heart has been proposed as a potential pathway for improving organ function in patients with severe heart failure. These tissue patches include surface patches, attached to the epicardium of the heart, and thick transmural patches that replace the infarcted region. However, little is known about the impact of cardiac tissue patches on pump function in a patient's heart. In addition, it is not clear what patch structural properties - such as active stress generation, muscle fiber alignment, or material stiffness - may best augment existing heart tissue. Computational modeling can be used to examine the influence of patch properties, illuminating the mechanical impact of cardiac tissue patches in the beating heart. In this work, we computationally implement different cardiac tissue patches to understand the benefits of particular patch types and properties. We find that in transmural cardiac tissue patches, both activation and fiber alignment improve function. A transmural patch generating 10% of healthy active stress can increase stroke volume by 18%, and higher generated active stress in a circumferential muscle fiber orientation can recover stroke volume by over 50%. Furthermore, we find that surface cardiac tissue patches can enhance heart function slightly despite limiting diastolic filling, especially when fibrotic thinning has occurred. These conclusions identify broad design goals for the engineering of cardiac tissue patches to improve heart function.

In cardiovascular mechanics, reaching consensus in simulation results within a physiologically relevant range of parameters is essential for reproducibility purposes. Although currently available benchmarks contain some of the features that cardiac mechanics models typically include, some important modeling aspects are missing. Therefore, we propose a new set of cardiac benchmark problems and solutions for assessing passive and active material behavior, viscous effects, and pericardial boundary condition. The problems proposed include simplified analytical fiber definitions and active stress models on a monoventricular and biventricular domains, allowing straightforward testing and validation with already developed solvers.

To broaden efforts for improving diversity, equity, and inclusion (DEI) in biomedical engineering (BME) education—a key area of emphasis is the integration of inclusive teaching practices. While BME faculty generally support these efforts, translating support into action remains challenging. This project aimed to address this need through a 3-phase inclusive teaching training, consisting of graduate students, faculty, and engineering education consultants. In Phase I, graduate students and faculty participated in a 6-week learning community on inclusive teaching ( Foundational Learning ). In Phase II, graduate students were paired with faculty to modify or develop new inclusive teaching materials to be integrated into a BME course ( Experiential Learning ). Phase III was the implementation of these materials. To assess Phases I & II, graduate student participants reflected on their experiences on the project. To assess Phase III, surveys were administered to students in IT-BME-affiliated courses as well as those taking other BME-related courses. Phases I & II: graduate students responded positively to the opportunity to engage in this inclusive teaching experiential learning opportunity. Phase III: survey results indicated that the incorporation of inclusive teaching practices in BME courses enhanced the student learning experience. The IT-BME project supported graduate students and faculty in learning about, creating, and implementing inclusive teaching practices in a collaborative and supportive environment. This project will serve to both train the next class of instructors and use their study of inclusive teaching concepts to facilitate the creation of ideas and materials that will benefit the BME curriculum and students.

The mechanical function of the myocardium is defined by cardiomyocyte contractility and the biomechanics of the extracellular matrix (ECM). Understanding this relationship remains an important unmet challenge due to limitations in existing approaches for engineering myocardial tissue. Here, they established arrays of cardiac microtissues with tunable mechanics and architecture by integrating ECM-mimetic synthetic, fiber matrices, and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), enabling real-time contractility readouts, in-depth structural assessment, and tissue-specific computational modeling. They found that the stiffness and alignment of matrix fibers distinctly affect the structural development and contractile function of pure iPSC-CM tissues. Further examination into the impact of fibrous matrix stiffness enabled by computational models and quantitative immunofluorescence implicates cell-ECM interactions in myofibril assembly, myofibril maturation, and notably costamere assembly, which correlates with improved contractile function of tissues. These results highlight how iPSC-CM tissue models with controllable architecture and mechanics can elucidate mechanisms of tissue maturation and disease.

Movies of human induced pluripotent stem cell (hiPSC)-derived engineered cardiac tissue (microbundles) contain abundant information about structural and functional maturity. However, extracting these data in a reproducible and high-throughput manner remains a major challenge. Furthermore, it is not straightforward to make direct quantitative comparisons across the multiple in vitro experimental platforms employed to fabricate these tissues. Here, we present "MicroBundlePillarTrack," an open-source optical flow-based package developed in Python to track the deflection of pillars in cardiac microbundles grown on experimental platforms with two different pillar designs ("Type 1" and "Type 2" design). Our software is able to automatically segment the pillars, track their displacements, and output time-dependent metrics for contractility analysis, including beating amplitude and rate, contractile force, and tissue stress. Because this software is fully automated, it will allow for both faster and more reproducible analyses of larger datasets and it will enable more reliable cross-platform comparisons as compared to existing approaches that require manual steps and are tailored to a specific experimental platform. To complement this open-source software, we share a dataset of 1,540 brightfield example movies on which we have tested our software. Through sharing this data and software, our goal is to directly enable quantitative comparisons across labs, and facilitate future collective progress via the biomedical engineering open-source data and software ecosystem.

Engineered heart tissues (EHTs) are a promising technology to improve cardiac disease treatment. However, manufacturing EHTs with mature iPSC-CMs remains a challenge due to, in part, our lack of understanding of the mechanobiology of these tissues. Developing computational models can help elucidate the complex interplay between the extracellular matrix, the boundary conditions, and the myofibril development in EHTs. In order to accurately represent EHT mechanics, it is necessary to understand what biophysical properties are relevant. In this study, we focus on studying the impact of the length-dependent activation (LDA) property in EHTs, which is known to be crucial in whole heart mechanics. Using a biomechanical computational model, we show that when LDA is considered, the simulated displacements are more accurate, and the stretches are more spatially homogeneous. Furthermore, the LDA modifies how different parameters impact the tissue's mechanical performance, and it is, therefore, crucial to EHT modeling.

Advancing human induced pluripotent stem cell derived cardiomyocyte (hiPSC-CM) technology will lead to significant progress ranging from disease modeling, to drug discovery, to regenerative tissue engineering. Yet, alongside these potential opportunities comes a critical challenge: attaining mature hiPSC-CM tissues. At present, there are multiple techniques to promote maturity of hiPSC-CMs including physical platforms and cell culture protocols. However, when it comes to making quantitative comparisons of functional behavior, there are limited options for reliably and reproducibly computing functional metrics that are suitable for direct cross-system comparison. In addition, the current standard functional metrics obtained from time-lapse images of cardiac microbundle contraction reported in the field (i.e., post forces, average tissue stress) do not take full advantage of the available information present in these data (i.e., full-field tissue displacements and strains). Thus, we present “MicroBundleCompute,” a computational framework for automatic quantification of morphology-based mechanical metrics from movies of cardiac microbundles. Briefly, this computational framework offers tools for automatic tissue segmentation, tracking, and analysis of brightfield and phase contrast movies of beating cardiac microbundles. It is straightforward to implement, runs without user intervention, requires minimal input parameter setting selection, and is computationally inexpensive. In this paper, we describe the methods underlying this computational framework, show the results of our extensive validation studies, and demonstrate the utility of exploring heterogeneous tissue deformations and strains as functional metrics. With this manuscript, we disseminate “MicroBundleCompute” as an open-source computational tool with the aim of making automated quantitative analysis of beating cardiac microbundles more accessible to the community.

Biomechanics plays an important role in the diagnosis and treatment of pathological conditions of the heart. Computational models are paving the way for personalized therapeutic treatment but they rely on accurate constitutive equations for predicting their biomechanical behavior. Even so, viscoelasticity remains under-explored in computational modeling despite experimental observations. To facilitate the viscoelastic modeling of cardiovascular soft tissues, we previously developed a fractional viscoelastic modeling approach, which extends existing hyperelastic models. This has comparable computational costs to the conventional hyperelastic model and only requires two additional material parameters for the viscoelastic response. This approach was demonstrated to be able to accurately capture the viscoelastic response of the human myocardium. However, the numerical properties of this fractional viscoelastic approach have not yet been examined. In this work, we present its implementation in Finite Element Analysis, examine its numerical properties in uniaxial extension and 2D inflation test examples, and examine its physiological implication in a computational model of an idealized left ventricle in a fully idealized circulatory system. Optimal convergence properties were observed and the importance of viscoelasticity during passive filling, ventricular motion, and regional fiber strain and stresses were explained.

Engineered heart tissues (EHTs) present a potential solution to some of the current challenges in the treatment of heart disease; however, the development of mature, adult-like cardiac tissues remains elusive. Mechanical stimuli have been observed to improve whole-tissue function and cardiomyocyte (CM) maturation, although our ability to fully utilize these mechanisms is hampered, in part, by our incomplete understanding of the mechanobiology of EHTs. In this work, we leverage the experimental data produced by a mechanically tunable experimental setup to generate tissue-specific computational models of EHTs. Using imaging and functional data, our modeling pipeline generates models with tissue-specific ECM and myofibril structure, allowing us to estimate CM active stress. We use this experimental and modeling pipeline to study different mechanical environments, where we contrast the force output of the tissue with the computed active stress of CMs. We show that the significant differences in measured experimental forces can largely be explained by the levels of myofibril formation achieved by the CMs in the distinct mechanical environments, with active stress showing more muted variations across conditions. The presented model also enables us to dissect the relative contributions of myofibrils and extracellular matrix to tissue force output, a task difficult to address experimentally. These results highlight the importance of tissue-specific modeling to augment EHT experiments, providing deeper insights into the mechanobiology driving EHT function.

Introduction: The mechanical function of the myocardium is dictated by cardiomyocytes (CMs) and the surrounding fibrous extracellular matrix (ECM). Due to limitations in existing approaches for engineering myocardial tissue, how CMs sense and respond to mechanical microenvironmental changes remains understudied. Here, we established a method for creating arrays of cardiac microtissues with highly tunable mechanical features by integrating synthetic, fibrous matrices and iPSC-CMs. Through real-time contractility readouts enabled, we explored how biomechanical cues influence engineered cardiac tissue assembly and function. Methods: Arrayed cardiac microtissues were generated by selectively photo-crosslinking electrospun dextran vinyl sulfone fiber matrices onto pairs of microfabricated PDMS cantilevers. Subsequently, iPSC-CMs were patterned onto matrices using a microfabricated seeding mask. Matrix and cantilever stiffnesses were tuned by adjusting photoinitiator concentrations and cantilever geometry, respectively. Results: In tissues contracting against soft cantilevers (0.41 N/m), tissue contractile stress decreased with increasing matrix stiffness (E = 0.68 to 17.3 kPa). We also observed an increase in contractile stress on soft and aligned matrices, compared to non-aligned matrices. However, tissues contracting against stiffer cantilevers (1.2 N/m) exerted similar contractile stresses independent of matrix alignment. This may be explained by the enhanced alignment of myofibrils in non-aligned matrices contracting against stiffer posts. The size of vinculin-rich cell-ECM adhesion decreased on stiff matrices. Vinculin also localized to z-discs forming organized costameres on soft matrices. Tissue response to mavacamten suggests that β-cardiac myosins are required for costamere formation and myofibril maturation. Conclusions: We developed a new microfabrication strategy to create tunable cardiac microtissues and examined how biomechanical cues affect tissue assembly and function. We found that matrix stiffness, matrix alignment, and tissue constraint distinctly affect iPSC-CM function and structure, potentially due to differential influences on costamere formation and myofibrillar assembly.

Simulations of cardiac electrophysiology and mechanics have been reported to be sensitive to the microstructural anisotropy of the myocardium. Consequently, a personalized representation of cardiac microstructure is a crucial component of accurate, personalized cardiac biomechanical models. In-vivo cardiac Diffusion Tensor Imaging (cDTI) is a non-invasive magnetic resonance imaging technique capable of probing the heart's microstructure. Being a rather novel technique, issues such as low resolution, signal-to noise ratio, and spatial coverage are currently limiting factors. We outline four interpolation techniques with varying degrees of data fidelity, different amounts of smoothing strength, and varying representation error to bridge the gap between the sparse in-vivo data and the model, requiring a 3D representation of microstructure across the myocardium. We provide a workflow to incorporate in-vivo myofiber orientation into a left ventricular model and demonstrate that personalized modelling based on fiber orientations from in-vivo cDTI data is feasible. The interpolation error is correlated with a trend in personalized parameters and simulated physiological parameters, strains, and ventricular twist. This trend in simulation results is consistent across material parameter settings and therefore corresponds to a bias introduced by the interpolation method. This study suggests that using a tensor interpolation approach to personalize microstructure with in-vivo cDTI data, reduces the fiber uncertainty and thereby the bias in the simulation results.

The hierarchical construction of the myocardium plays a pivotal role in the biomechanics of the heart muscle and the resulting flow of blood. In disease, the construction of the heart remodels, altering the structure of the tissue from the subcellular level all the way to the whole organ. Elucidating the impact of these fundamental alterations on the biomechanics of the heart presents challenges to diagnosis, therapy planning, and treatment. Computational modeling provides an innovative tool, enabling the simulation of complex biomechanics that capture the complexity of tissue, its growth and remodeling, and the resulting blood flow. In this chapter, we review the key ways that computational models can address challenging biomechanical questions in the heart and how these tools can change the way treatment is approached across a range of heart diseases.

Gap junctions are key mediators of intercellular communication in cardiac tissue, and their function is vital to sustaining normal cardiac electrical activity. Conduction through gap junctions strongly depends on the hemichannel arrangement and transjunctional voltage, rendering the intercellular conductance highly non-Ohmic, particularly under steady-state regimes of conduction. Despite this marked non-linear behavior, current tissue-level models of cardiac conduction are rooted in the assumption that gap-junctions conductance is constant (Ohmic), which results in inaccurate predictions of electrical propagation, particularly in the low junctional-coupling regime observed under pathological conditions. In this work, we present a novel non-Ohmic homogenization model (NOHM) of cardiac conduction that is suitable to tissue-scale simulations. Using non-linear homogenization theory, we develop a conductivity model that seamlessly upscales the voltage-dependent conductance of gap junctions, without the need of explicitly modeling gap junctions. The NOHM model allows for the simulation of electrical propagation in tissue-level cardiac domains that accurately resemble that of cell-based microscopic models for a wide range of junctional coupling scenarios, recovering key conduction features at a fraction of the computational complexity. A unique feature of the NOHM model is the possibility of upscaling the response of non-symmetric gap-junction conductance distributions, which result in conduction velocities that strongly depend on the direction of propagation, thus allowing to model the normal and retrograde conduction observed in certain regions of the heart. We envision that the NOHM model will enable organ-level simulations that are informed by sub- and inter-cellular mechanisms, delivering an accurate and predictive in-silico tool for understanding the heart function. Codes are available for download at https://github.com/dehurtado/NonOhmicConduction.