Lik Chuan Lee

We present a computational modeling framework coupling a poroelastic left ventricle (LV) with lumped‐parameter descriptions of the coronary and systemic circulations in a closed‐loop system. The finite element method, with P1‐P1‐P0 discretization of the displacement, Darcy flow and pore‐pressure fields, is used to solve the boundary value problem associated with the poroelastic formulation. We show that simulation of the isolated non‐contractile poroelastic LV can reproduce perfusion‐induced stiffening features found in measurements of pressure‐volume relationships with less than 1 mmHg error. Simulations coupling the poroelastic LV with systemic and coronary circulatory models also show that the framework can produce phasic changes in coronary flow rate and myocardial wall volume that agree with the experiments. Using the framework, we investigate the effects of coronary sinus (CS) resistance on myocardial perfusion. We show that increasing CS resistance attenuates coronary flow rate, increases myocardial pore pressure, and promotes myocardial swelling.

Purpose: Subject-specific three-dimensional models of the coronary arterial tree are widely used for simulations of coronary flow and myocardial perfusion. Current image-based reconstructions, however, cannot capture the full microvascular hierarchy, whereas existing synthetic tree generation methods either operate in idealized left ventricular geometries or lack consistency with experimental vessel hierarchy when constrained by anatomy. Methods: To address these issues, we present a hierarchy-aware CT-driven constrained constructive optimization (HCT-CCO) framework that generates coronary arterial trees as extensions from segmented subject-specific base epicardial coronary networks. The method combines a distance field that restricts candidate segments to the segmented myocardial wall, applies Kamiya-type optimization seeded with HK-type daughter radii and a flow-weighted bifurcation point, and a hierarchy-aware, stage-wise branching schedule that progressively shifts growth toward distal segments as the domain fills. The resulting networks are assigned diameter-defined Strahler orders and analyzed in terms of diameter–order scaling, order–order connectivity, and transmural segment distributions. Results: We show that the HCT-CCO algorithm is more stable across stochastic realizations and produces connectivity matrices and diameter–order curves that show improved agreement with experimental benchmarks compared to the conventional CCO algorithm. Application to one synthetic case and five subject-specific CT datasets yielded coronary trees spanning diameter-defined Strahler orders 1–11 with monotonic diameter–order trends and mean diameters consistent with measured porcine coronary morphometry and transmural segment distributions across cases. Conclusion: The HCT-CCO algorithm thus provides a hierarchy-aware, image-constrained approach for generating subject-specific coronary arterial networks that jointly capture experimentally supported diameter–order relations, hierarchical connectivity, and transmural vessel distributions, and offers anatomically grounded substrates for coronary hemodynamic and perfusion modeling.

Purpose Myocardial reactive interstitial fibrosis (RIF) is a common feature in heart failure (HF) and is associated with changes in the structure and function of the heart. The impact of interstitial space enlargement and accumulation of interstitial collagen on oxygen transport from capillaries to cardiomyocytes during RIF is not fully understood. Methods To address this issue, we developed a histology-image-based computer modeling approach to estimate the spatial profile of pO₂ in tissue samples from the myocardium of healthy dogs and dogs with HF. Results The analysis shows that both interstitial space enlargement and RIF significantly limit tissue oxygenation, as evidenced by a ~ 47% lower pO₂ in cardiomyocytes from HF dogs compared to normal dogs. Tissue hypoxia is primarily driven by the enlargement of interstitial space for severe RIF. The reduction in tissue pO₂ for mild and moderate RIF, however, is primarily driven by the fibrosis-related functional changes in the oxygen diffusivity and maximum oxygen consumption rates. Multivariate linear regression shows cardiomyocyte pO₂ significantly drops with interstitial area fraction (IAF) and rises with capillary area fraction (CAF) (R² = 0.761, p = 0.003). Our analysis suggests the strong performance of the CAF/IAF ratio in predicting tissue oxygenation (r = 0.91, R² = 0.824, p = 0.0001) through the coupling of interstitial expansion-impaired diffusion with reduced microvascular oxygen supply per tissue volume. Conclusion These findings provide quantitative evidence that interstitial space enlargement and RIF substantially affect O₂ transport from capillaries to cardiomyocytes in HF that could lead to significant regional tissue hypoxia.

Cardiac and hemodynamic conditions such as myocardial infarct, cardiomyopathy, hypertension, and aortic valve disease can impair conduction within the Purkinje fiber network and compromise left ventricular (LV) pump function. We developed a computational framework that couples electrical propagation in a structurally organized Purkinje fiber network with LV electromechanics to analyze the impact of conduction abnormalities on cardiac performance. A baseline simulation reproduced physiological activation patterns and pump indices consistent with healthy human data. Conduction block was then introduced at different locations within the Purkinje fiber network. LV pump function was strongly dependent on block location: left bundle branch block (LBBB) produced the largest reduction in ejection fraction (EF) (59% to 46%) and peak pressure (119 to 97 mmHg), whereas left anterior fascicle block caused smaller functional changes. Across simulations, myocardial activation delay and systolic dyssynchrony index (SDI) exhibited a nonlinear relationship with EF and myocardial strain. A threshold behavior was identified at a simulated LV activation duration of approximately 240 ms and an SDI of 8.4%, beyond which EF and strain decreased by about 5% relative to baseline. These findings provide a mechanistic framework to investigate how Purkinje fiber network conduction abnormalities influence LV pump dysfunction.

Cardiac resynchronization therapy (CRT) is a common intervention for patients with dyssynchronous heart failure, yet approximately one-third of recipients fail to respond, partly due to suboptimal lead placement. Identifying optimal pacing sites remains challenging, largely due to patient-specific anatomical variability and limitations of current individualized planning strategies. In a step toward an in-silico approach, we develop two geometric deep learning models, based on graph neural network (GNN) and geometry-informed neural operator (GINO), to predict activation time maps on left ventricular (LV) geometries in real time. Trained on a large dataset generated from finite-element simulations spanning a wide range of synthetic LV shapes, pacing site configurations, and tissue conductivities, the GINO model outperforms the GNN on synthetic cases (1.38% vs 2.44% error), while both demonstrate comparable performance on real-world LV geometries (GINO: 4.79% vs GNN: 4.07%). Using the trained models, we develop a workflow to identify an optimal pacing site on the LV from a given activation time map and show that both models can robustly recover ground-truth subject-specific parameters from noisy inputs. In conjunction with an interactive web-based interface (https://dcsim.egr.msu.edu/), this study shows potential and motivates future extension toward a clinical decision-support tool for personalized pre-procedural CRT optimization.

Mitral valve orifice area is a key functional metric that depends on complex geometric features, motivating a systematic assessment of the relative influence of these parameters. In this study, the mitral valve geometry is parameterized using twelve geometric variables, and a global sensitivity analysis based on Sobol indices is performed to quantify their relative importance. Because global sensitivity analysis requires many simulations, a Gaussian Process regressor is developed to efficiently predict the orifice area from the geometric inputs. Structural simulations of the mitral valve are carried out in Abaqus, focusing exclusively on the valve mechanics. The predicted distribution of orifice areas obtained from the Gaussian Process shows strong agreement with the ground-truth simulation results, and similar agreement is observed when only the most influential geometric parameters are varied. The analysis identifies a subset of geometric parameters that dominantly govern the mitral valve orifice area and can be reliably extracted from medical imaging modalities such as echocardiography. These findings establish a direct link between echocardiographic measurements and physics-based simulations and provide a framework for patient-specific assessment of mitral valve mechanics, with potential applications in guiding interventional strategies such as MitraClip placement.