Vijay Vedula

Abstract The control of uterine activity during pregnancy is a complex process that involves regulating myometrial excitability across multiple scales. While numerous studies have investigated various regulatory mechanisms and established the contributions of ion channels and gap junctions, how these mechanisms interact to produce observed changes in uterine activity remains poorly understood. Pivotal to these efforts are computational models that effectively capture gestational changes in excitability across scales. In this study, we propose a multiscale computational modeling framework that can reproduce measured activity at the cellular and tissue scales at a given gestational stage. At the cellular level, we identify key ion currents underlying the observed electrophysiological properties based on a literature review of their regulation and a sensitivity analysis of the Tong 2011 uterine smooth muscle cell activation model. The conductances of these ion currents are then fit to reproduce characteristic resting membrane potentials and burst properties using Bayesian optimization. To extend to the tissue level, we employ an anisotropic monodomain model, parameterized by the resistivity of late pregnancy uterine muscle, to investigate electrical propagation in a two-dimensional section of uterine tissue. We then apply the multiscale model to study myometrial activation in late pregnancy and elucidate the contributions of ion channel and gap junction regulation in transitioning the uterus from a quiescent state to labor. Our resulting model successfully reproduces measured electrophysiological properties at the cellular level and characteristic single-spike and burst-propagation patterns at the tissue level across the three late-pregnant time points analyzed (days 16/17, 18/19, and 20/21) in a murine model. Furthermore, our results suggest that the regulation of the conductances of the voltage-dependent potassium current (I K1 ), L-type calcium current (I CaL ), and sodium current (I Na ) is most important in determining preterm uterine excitability. The framework established here will promote the development of more gestationally relevant models to better understand labor progression and the factors involved in dysfunctional labor. Author Summary Pregnancy is marked by drastic changes in the electrical and contractile activity of the uterus. As improper regulation of uterine activity is associated with preterm and dysfunctional labor, it is crucial to understand the physiological mechanisms underlying these changes. Currently, the roles of ion channels in determining cellular dynamics, and gap junctions in cell-to-cell coupling, as well as tissue properties, have been well established. However, how their regulation interacts to produce observed changes in cellular and tissue excitability and, in turn, organ-level activity is far less understood. While existing computational models of uterine electrophysiology have provided a greater insight into these processes, these are formulated for a single time point and cannot interrogate their effects over gestation. In response to this need, we develop a framework to generate a computational model of uterine excitation at a given gestational stage. We apply this to investigate the role of ion channels and gap junctions in transitioning the uterus to labor during late pregnancy. We identify three major ion channels and demonstrate agreement with observed action potential and tissue propagation properties at the analyzed time points. We further highlight how our framework can be applied to investigate other stages, including labor and postpartum.

Purpose: Patient-specific models of left atrial (LA) mechanics often assume uniform left atrial wall thickness (LAWT), but the effect of LAWT on the mechanics and hemodynamics remains less quantified. Methods: Four LA myocardium models were built from gated CTA images: a baseline variable thickness (VT#0), two reduced-dilation variants, and a 2mm uniform thickness model. Multi-scale mechanics and blood flow simulations were performed across all the thickness variants using model parameters personalized on the baseline model. Predicted displacements, wall stresses and strains, and hemodynamics were compared. Results: Across all LAWT variants, myocardial volume spanned 14.4-19.9mL (38%), while cavity volume remained mostly within 5% of image data throughout the cardiac cycle. Circulatory system output, myocardial displacements, and strains varied by 5-6% relative to the baseline model. Instantaneous stresses increased by up to 19% in the thinner variable thickness models and decreased by up to 16% in the uniformly thick case. Globally, the area under low time-averaged wall shear stress (TAWSS) varied between 23% and 30% across all thickness variants, while LA exposed to elevated oscillatory shear index (OSI) increased from nearly 6% to 19%. Over 90% of LAA was exposed to low shear, but the high-OSI area increased from 7% in VT#0 to over 30% in Uniform. Conclusion: A personalized multiscale modeling framework was leveraged to demonstrate that the left atrial myocardial stresses and oscillatory shear had a greater sensitivity to local wall thickness representation compared to cavity volumes, tissue displacements, strains, and mean blood shear.

Finite element analysis (FEA) is a powerful tool that forms the cornerstone of modeling cardiac biomechanics. However, FEA is computationally expensive for creating digital twins, which typically involves performing tens or hundreds of FEA simulations to estimate tissue parameters, limiting its clinical application. We have developed an attention-enhanced graph neural network (GNN)-based FEA emulator, HeartSimSage, to rapidly predict passive biventricular myocardial displacements from patient-specific geometries, chamber pressures, and material properties. Designed to overcome the limitations of current emulators, HeartSimSage can effectively handle diverse three-dimensional (3D) biventricular geometries, mesh topologies, fiber directions, structurally based constitutive models, and physiological boundary conditions. It also supports flexible mesh structures, allowing variable node count, ordering, and element connectivity. To optimize information propagation, we developed a neighboring connection strategy inspired by Graph Sample and Aggregate (GraphSAGE) that prioritizes local node interactions while maintaining mid-to-long-range dependencies. Additionally, we integrated Laplace-Dirichlet solutions to enhance spatial encoding and employed subset-based training to improve computational efficiency. Incorporating the attention mechanism allows HeartSimSage to adaptively weigh neighbor contributions and filter out irrelevant information flow, enhancing prediction accuracy. As a result, errors in the predicted biventricular myocardial displacements by HeartSimSage were limited to a median of 0.280 mm with an interquartile range of [0.167, 0.484] mm compared to traditional FEA, while achieving a computational speedup of approximately 13000X on a GPU and 190X on a CPU. We validated our model on a published left ventricle dataset and analyzed the model's sensitivity to hyperparameters, neighboring connection strategies, and the attention mechanism.

Abstract Purpose To evaluate the hemodynamic impact of restoring a normal sino-tubular junction (STJ) following a novel Hegar dilator-based procedure in patients undergoing root-sparing ascending thoracic aortic aneurysm (ATAA) repair using computational modeling. Methods We retrospectively selected an ATAA patient who underwent pre- and postoperative gated computed tomography angiography (CTA). We developed a novel workflow to segment the lumen, thick-walled aorta, and aortic valve from CTA images for subsequent blood flow analysis using computational fluid dynamics (CFD) and fluid-structure interaction (FSI). Morphological and hemodynamic characteristics of the root were quantified and compared against those of a control subject, with no noted ascending aortic dilation. The model’s sensitivity to graft properties and leaflet material heterogeneity was analyzed. Results Both CFD and FSI results showed that the postoperative geometry reconstructed with a normal STJ profile reintroduces sinus vortices during peak systole, similar to the control subject, but were absent pre-surgery. Accounting for aortic valve leaflets in FSI studies yielded qualitatively similar results to the CFD cases, albeit with locally elevated velocities, time-averaged wall shear stress (TAWSS), and energy dissipation, likely due to the dynamically changing orifice area and differing profiles of the left ventricular outflow tract (LVOT). Conclusion We demonstrated that the novel Hegar dilator-based STJ reconstruction restores normal blood flow patterns, highlighting the importance of reprofiling the aortic sinuses and STJ. The study also highlights the model’s sensitivities, particularly the LVOT shape and leaflet morphology and mobility, and may assist planning STJ reconstruction to yield optimal hemodynamics before intervention.

We present a computational framework for constructing patient-specific models of cardiac mechanics based on standard clinical data, including electrocardiogram (ECG), cuff blood pressure, and electrocardiography-gated computed tomography angiography (CTA) imaging. The model is coupled to a closed-loop lumped parameter network (LPN) circulatory model and incorporates rule-based fiber architecture, as well as spatially varying epicardial boundary conditions to approximate surrounding tissue support. Model parameters are personalized through a multistep procedure that sequentially tunes circulatory dynamics, passive mechanics, and active contraction. The resulting personalized BiV model closely matches clinical pressure and volume measurements and reasonably agrees with image-based myocardial deformation. To assess the impact of anatomical model choice, we compare the BiV model to two commonly-used simplifications: a truncated BiV (t-BiV) model cut at the basal plane and a left ventricle-only (LV) model. For these models, we also evaluate their sensitivity to plausible variations in boundary conditions and contractile strength. With all other inputs held fixed, the LV model exhibits similar global pressure/volume behavior, despite moderate differences in regional deformation. In contrast, the t-BiV model produces substantial differences in both global function and local myocardial mechanics. These results suggest that while LV-only models may be sufficient for biomechanical studies, truncation at the basal plane strongly impacts model outputs and should be used with caution.

ABSTRACT Purpose Borderline left ventricle (BLV) presents a dilemma between pursuing a biventricular repair (BiVR) and a Stage 1 palliation (S1P) because a discordant pursuit of BiVR increases mortality risk. We aim to develop and validate a personalized computational model to assist surgical decision-making by predicting virtual surgery hemodynamics in BLV patients. Methods We developed a novel multi-block lumped parameter network (LPN) model of a BLV circulatory system. Patient-specific model parameters were estimated using a semi-automatic tuning framework to fit clinical data in ten retrospectively identified BLV patients. Virtual surgeries (BiVR and S1P) were performed on each patient to quantify post-operative hemodynamics. Results In patients who clinically received S1P (Group I, N=5), a virtual BiVR predicted significantly elevated mean pulmonary artery pressure (PAP mean : 38.00±10.0 vs. 17.50±2.7 mmHg, p <0.01), mean left atrial pressure (LAP mean : 25.40±8.2 vs. 6.20±1.2 mmHg, p <0.0001), and single ventricle end-diastolic pressure (SVEDP: 21.80±8.7 vs. 4.80±1.3 mmHg, p <0.0001) compared with a virtual S1P. A virtual BiVR in patients who clinically underwent BiVR (Group II, N=5) did not predict any adverse hemodynamic outcome. Conclusions A novel digital twinning framework was developed to predict hemodynamics following virtual surgeries in BLV patients. The model predictions align with the clinically adopted procedure in this retrospectively selected cohort by predicting unacceptable PAP, LAP, and SVEDP. This predictive tool may guide surgeons in determining the hemodynamically optimal surgery for BLV infants, but it needs prospective validation. CENTRAL MESSAGE Patient-specific computational modeling can predict hemodynamics following virtual surgery in borderline left ventricles and may assist surgical decision-making. PERSPECTIVE A critical dilemma pediatric heart surgeons and pediatric cardiologists face is choosing between biventricular repair and single ventricle palliation in patients born with a borderline left ventricle. Computational modeling using lumped parameter networks predicts hemodynamics from virtual surgery simulations and may enable clinicians to decide on the hemodynamically optimal procedure.

Abstract We present a personalized multiscale mechanics model of the left atrium (LA) to simulate its deformation throughout the cardiac cycle and drive blood flow. Our patient data-driven model tightly integrates 3D structural mechanics of the LA myocardium, incorporating both passive and active components, with a 0D closed-loop lumped parameter network (LPN)-based circulatory system model. A finite element (FE) model of LA tissue is constructed from the patient’s images, assuming uniform thickness and employing rule-based fiber directions, a structurally based constitutive model for the passive mechanics, and a phenomenological contraction model while applying physiologically relevant boundary conditions. We then adopted a multi-step personalization approach, in which the LPN parameters with a surrogate LA model are first optimized to match cuff-based blood pressures and cardiac lumen volumes derived from time-resolved 3D gated computed tomography angiography (CTA) images. The surrogate LA pressure during passive expansion is used to estimate myocardial passive mechanics parameters and the reference unloaded configuration using an inverse finite element analysis (iFEA) framework. Finally, a robust multiscale coupling is applied between the iFEA-optimized FE model and the tuned 0D LPN model to characterize LA contraction. This effectively captures the 8-shaped pressure-volume curve and reasonably aligns with the image-based cavity volumes and deformation. The resulting simulation-predicted deformation is imposed as a moving-wall boundary condition to model atrial hemodynamics. Overall, this comprehensive digital twinning platform could be applied to study LA biomechanics in health and disease and assist in devising personalized treatment plans.

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.

Personalized cardiac mechanics modeling is a powerful tool for understanding the biomechanics of cardiac function in health and disease and assisting in treatment planning. However, current models are limited to using medical images acquired at a single cardiac phase, often limiting their applicability for processing dynamic image acquisitions. This study introduces an inverse finite element analysis (iFEA) framework to estimate the passive mechanical properties of cardiac tissue using time-dependent medical image data. The iFEA framework relies on a novel nested optimization scheme, in which the outer iterations utilize a traditional optimization method to best approximate material parameters that fit image data, while the inner iterations employ an augmented Sellier's algorithm to estimate the stress-free reference configuration. With a focus on characterizing the passive mechanical behavior, the framework employs structurally based anisotropic hyperelastic constitutive models and physiologically relevant boundary conditions to simulate myocardial mechanics. We use a stabilized variational multiscale formulation for solving the governing nonlinear elastodynamics equations, verified for cardiac mechanics applications. The framework is tested in myocardium models of biventricle and left atrium derived from cardiac phase-resolved computed tomographic (CT) images of a healthy subject and three patients with hypertrophic obstructive cardiomyopathy (HOCM). The impact of the choice of optimization methods and other numerical settings, including fiber direction parameters, mesh size, initial parameters for optimization, and perturbations to optimal material parameters, is assessed using a rigorous sensitivity analysis. The performance of the current iFEA is compared against an assumed power-law-based pressure-volume relation, typically used for single-phase image acquisition.

Background and Objective: Blood flow assessment is an emerging technique that allows for assessment of hemodynamics in the heart and blood vessels. Recent advances in cardiovascular imaging technologies have made it possible for this technique to be more accessible to clinicians and researchers. Blood flow assessment typically refers to two techniques: measurement-based flow visualization using echocardiography or four-dimensional flow magnetic resonance imaging (4D flow MRI), and computer-based flow simulation based on computational fluid dynamics modeling. Using these methods, blood flow patterns can be visualized and quantitative measurements of mechanical stress on the walls of the ventricles and blood vessels, most notably the aorta, can be made. Thus, blood flow assessment has been enhancing the understanding of cardiac and aortic diseases; however, its introduction to clinical practice has been negligible yet. In this article, we aim to discuss the clinical applications and future directions of blood flow assessment in aortic surgery. We then provide our unique perspective on the technique's translational impact on the surgical management of aortic disease. Methods: Articles from the PubMed database and Google Scholar regarding blood flow assessment in aortic surgery were reviewed. For the initial search, articles published between 2013 and 2023 were prioritized, including original articles, clinical trials, case reports, and reviews. Following the initial search, additional articles were considered based on manual searches of the references from the retrieved literature. Key Content and Findings: In aortic root pathology and ascending aortic aneurysms, blood flow assessment can elucidate postoperative hemodynamic changes after surgical reconfiguration of the aortic valve complex or ascending aorta. In cases of aortic dissection, analysis of blood flow can predict future aortic dilatation. For complicated congenital aortic anomalies, surgeons may use preoperative imaging to perform "virtual surgery", in which blood flow assessment can predict postoperative hemodynamics for different surgical reconstructions and assist in procedural planning even before entering the operating room. Conclusions: Blood flow assessment and computational modeling can evaluate hemodynamics and flow patterns by visualizing blood flow and calculating biomechanical forces in patients with aortic disease. We anticipate that blood flow assessment will become an essential tool in the treatment planning and understanding of the progression of aortic disease.

Background: Systolic anterior motion (SAM) of the mitral valve can result in mitral regurgitation (MR) and adverse outcomes in patients with obstructive hypertrophic cardiomyopathy (HCM). However, the mechanism and characteristics of MR severity mediated by SAM are unresolved. This study aimed to elucidate the anatomic and hemodynamic associations of MR and the impact of septal myectomy on changes in MR severity in patients with HCM. Methods: We retrospectively reviewed patients who underwent septal myectomy with SAM and interpretable imaging between 2017–2022. Significant MR was defined as moderate or more MR. The mitral valve, papillary muscle, and left ventricular geometry were quantitatively evaluated via echocardiography and cardiac computed tomography. Results: Out of 34 patients, two groups were identified: those with preoperative significant MR (n=16) and those without significant MR (n=18). Patients with significant preoperative MR exhibited worse heart failure symptoms at baseline than those without. Following myectomy, these patients showed higher residual left ventricular outflow tract (LVOT) gradients at rest and with provocative measures than those without preoperative MR. Multivariate regression analysis revealed a significant association between the tenting area and MR severity. Additionally, the chordal cutting procedure alleviated the tenting area [2.1 (1.8–2.6) vs. 1.4 (1.2–1.6) cm2] compared to those without it. Conclusions: Our preliminary data suggested that chordal cutting with septal myectomy was associated with an improvement in the tenting area, contributing to MR severity. This procedure may serve as an effective therapy for patients with SAM and significant MR.

In numerical simulations of cardiac mechanics, coupling the heart to a model of the circulatory system is essential for capturing physiological cardiac behavior. A popular and efficient technique is to use an electrical circuit analogy, known as a lumped parameter network or zero-dimensional (0D) fluid model, to represent blood flow throughout the cardiovascular system. Due to the strong physical interaction between the heart and the blood circulation, developing accurate and efficient numerical coupling methods remains an active area of research. In this work, we present a modular framework for implicitly coupling three-dimensional (3D) finite element simulations of cardiac mechanics to 0D models of blood circulation. The framework is modular in that the circulation model can be modified independently of the 3D finite element solver, and vice versa. The numerical scheme builds upon a previous work that combines 3D blood flow models with 0D circulation models (3D fluid - 0D fluid). Here, we extend it to couple 3D cardiac tissue mechanics models with 0D circulation models (3D structure - 0D fluid), showing that both mathematical problems can be solved within a unified coupling scheme. The effectiveness, temporal convergence, and computational cost of the algorithm are assessed through multiple examples relevant to the cardiovascular modeling community. Importantly, in an idealized left ventricle example, we show that the coupled model yields physiological pressure-volume loops and naturally recapitulates the isovolumic contraction and relaxation phases of the cardiac cycle without any additional numerical techniques. Furthermore, we provide a new derivation of the scheme inspired by the Approximate Newton Method of Chan (1985), explaining how the proposed numerical scheme combines the stability of monolithic approaches with the modularity and flexibility of partitioned approaches.

Front Cover: Currently available heart valve prostheses have no growth potential, requiring children with heart valve diseases to endure multiple valve replacement surgeries. In article number 2300011, David Kalfa, Jeffrey W. Kysar, Richard L. Li and co-workers develop the proof of concept of a polymeric valved conduit that can be expanded by transcatheter balloon dilation to accommodate patient growth.

Currently available heart valve prostheses have no growth potential, requiring children with heart valve diseases to endure multiple valve replacement surgeries with compounding risks. This study demonstrates the in vitro proof of concept of a biostable polymeric trileaflet valved conduit designed for surgical implantation and subsequent expansion via transcatheter balloon dilation to accommodate the growth of pediatric patients and delay or avoid repeated open-heart surgeries. The valved conduit is formed via dip molding using a polydimethylsiloxane-based polyurethane, a biocompatible material shown here to be capable of permanent stretching under mechanical loading. The valve leaflets are designed with an increased coaptation area to preserve valve competence at expanded diameters. Four 22 mm diameter valved conduits are tested in vitro for hydrodynamics, balloon dilated to new permanent diameters of 23.26 ± 0.38 mm, and then tested again. Upon further dilation, two valved conduits sustain leaflet tears, while the two surviving devices reach final diameters of 24.38 ± 0.19 mm. After each successful dilation, the valved conduits show increased effective orifice areas and decreased transvalvular pressure differentials while maintaining low regurgitation. These results demonstrate concept feasibility and motivate further development of a polymeric balloon-expandable device to replace valves in children and avoid reoperations.