Perry S. Choi

Elena Sabdy Martinez, Alexander D. Kaiser, Alexander K. Reed et al.

Patient-specific computational modeling has emerged as a powerful tool for surgical planning in complex congenital heart disease. One promising application is complex biventricular repair, which often requires construction of a custom intraventricular baffle to establish a physiologic left ventricle-to-aorta outflow pathway. In current practice, baffle geometry is designed and shaped intraoperatively and preoperative planning remains largely manual, limiting the ability to generate anatomically conformal, watertight models suitable for quantitative hemodynamic assessment. In this work, we present a semi-automated computational framework for the design and assessment of patient-specific intraventricular baffles. The method constructs an explicit VSD-to-aorta flow pathway, preserves native right ventricular geometry, and reshapes only the baffle region using section-wise area constraints along a physiologically aligned centerline. The resulting geometry is integrated into a closed, multi-labeled domain for computational fluid dynamics analysis. We retrospectively applied this framework to four patients with double outlet right ventricle (DORV) who previously underwent biventricular repair. For each case, a patient-specific baffle was generated and its hemodynamic performance was evaluated using CFD. Predicted pressure gradients across the reconstructed outflow were within clinically acceptable ranges and comparable to the patients' postoperative echocardiographs. This approach enables quantitative, pre-operative design and evaluation of candidate baffle geometries and provides a reproducible method for generating simulation-ready models. By combining physiologically constrained geometric design with CFD-based assessment, the framework represents a step toward computational, patient-specific decision support for biventricular flow restoration in a complex heterogeneous patient population.

Fanwei Kong, Sascha Stocker, Perry S. Choi et al.

Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural abnormalities, often requiring customized treatment plans for individual patients. Computational modeling and analysis of these unique cardiac anatomies can improve diagnosis and treatment planning and may ultimately lead to improved outcomes. Deep learning (DL) methods have demonstrated the potential to enable efficient treatment planning by automating cardiac segmentation and mesh construction for patients with normal cardiac anatomies. However, CHDs are often rare, making it challenging to acquire sufficiently large patient cohorts for training such DL models. Generative modeling of cardiac anatomies has the potential to fill this gap via the generation of virtual cohorts; however, prior approaches were largely designed for normal anatomies and cannot readily capture the significant topological variations seen in CHD patients. Therefore, we propose a type- and shape-disentangled generative approach suitable to capture the wide spectrum of cardiac anatomies observed in different CHD types and synthesize differently shaped cardiac anatomies that preserve the unique topology for specific CHD types. Our DL approach represents generic whole heart anatomies with CHD type-specific abnormalities implicitly using signed distance fields (SDF) based on CHD type diagnosis, which conveniently captures divergent anatomical variations across different types and represents meaningful intermediate CHD states. To capture the shape-specific variations, we then learn invertible deformations to morph the learned CHD type-specific anatomies and reconstruct patient-specific shapes. Our approach has the potential to augment the image-segmentation pairs for rarer CHD types for cardiac segmentation and generate cohorts of CHD cardiac meshes for computational simulation.

Fanwei Kong, Aaron Brown, Michael Loecher et al.

Intracardiac flow patterns are shaped by the coupled motion of the cardiac chambers and heart valves and provide important information about cardiac function. However, clinical flow imaging remains limited by exam times, noise, resolution, and incomplete details of the three-dimensional flow. Computational fluid dynamics (CFD) can potentially provide detailed flow quantification and predictive insight into treatment outcomes, but clinical translation requires frameworks that reproduce patient-specific measurements while balancing physiological realism, computational cost, and modeling effort. Herein, we present an image-based, patient-specific computational framework for simulating whole-heart intracardiac hemodynamics that balances physiological fidelity with computational efficiency. The framework first employs machine learning-based segmentation and mesh propagation to reconstruct moving cardiac anatomies from time-resolved images. CFD simulations are then performed to resolve blood flow in deforming domains, while resistive immersed surfaces (RIS) are used to model all four cardiac valves with physiologically realistic opening and closing dynamics. The framework was applied to model hemodynamics in a healthy adult and a pediatric patient with complex congenital heart disease (CHD). In the healthy case, the simulations reproduced physiologic pressure-volume behavior, valve timing, and ventricular vortex formation. In the CHD case, simulated chamber and vessel pressures showed agreement with cardiac catheterization measurements. Simulated flow fields were qualitatively consistent with 4D-Flow MRI, while providing higher-resolution visualization of flow structures that were partially obscured by imaging artifacts. Comparison between the healthy and CHD cases further revealed altered diastolic flow organization and elevated normalized viscous dissipation in the CHD heart.