arXiv subjects

Perry S. Choi

Publications and source records attributed to Perry S. Choi.

5 recordsLinked to original sources

arXiv2026

Image-Based Whole-Heart Cardiac Flow Simulations in Health and Congenital Heart Disease

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.

eess.IV↗

arXiv2026

Semi-Automated Generation and Hemodynamic Assessment of Surgical Baffle Geometry for Biventricular Repair

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.

cs.CE↗

arXiv2026

Simulations Predict Improved Valve Performance Without Direct Leaflet Intervention After Neonatal Truncus Arteriosus Repair

Truncus arteriosus (TA) is a rare and severe congenital heart disease. Quadricuspid valve morphology occurs in 25% of all TA patients and is linked to regurgitation and increased risk of re-operation. It remains unclear how hemodynamic changes after TA repair alter valve performance. This study simulated pre- and postoperative conditions in a neonatal TA patient to investigate valve performance without direct intervention. We hypothesize that valve performance before and after truncal repair can be predicted in-silico, matching in-vivo imaging and identifying mechanisms how hemodynamic changes after repair will reduce valve regurgitation without direct intervention. Pre- and postoperative CT images of a neonatal patient with quadricuspid valve were segmented. Free edge length and geometric height from the patient's echocardiogram were used to model the valve. For the preoperative condition, ventricular pressures were set equal modeling an unrestricted ventricular septal defect. Systemic and pulmonary resistances were tuned based on the patient's Qp:Qs ratio. For the postoperative condition, boundary conditions were modified to mimic patient-specific hemodynamics after TA repair. The preoperative simulation confirmed mild valve regurgitation seen in-vivo. Interaction between asymmetric flow and surrounding vessel resulted in asymmetric opening and closing. Poor central coaptation led to a central regurgitant jet toward the septum. Altered postoperative hemodynamics improved coaptation and eliminated regurgitation, as seen in-vivo. This modeling approach reproduced in-vivo pre- and postoperative valve performance and identified mechanisms improving coaptation after TA repair. TA repair led to elimination of regurgitation due to enhanced central coaptation. Thus, altered postoperative hemodynamic conditions after TA repair may improve valve performance without direct leaflet intervention.

q-bio.TO↗

arXiv2023

SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects

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.

q-bio.TO↗

arXiv2023

Simulation-Based Design of Bicuspidization of the Aortic Valve

Objective: Severe congenital aortic valve pathology in the growing patient remains a challenging clinical scenario. Bicuspidization of the diseased aortic valve has proven to be a promising repair technique with acceptable durability. However, most understanding of the procedure is empirical and retrospective. This work seeks to design the optimal gross morphology associated with surgical bicuspidization with simulations, based on the hypothesis that modifications to the free edge length cause or relieve stenosis. Methods: Model bicuspid valves were constructed with varying free edge lengths and gross morphology. Fluid-structure interaction simulations were conducted in a single patient-specific model geometry. The models were evaluated for primary targets of stenosis and regurgitation. Secondary targets were assessed and included qualitative hemodynamics, geometric height, effective height, orifice area and billow. Results: Stenosis decreased with increasing free edge length and was pronounced with free edge length $\leq$1.3 times the annular diameter d. With free edge length 1.5d or greater, no stenosis occurred. All models were free of regurgitation. Substantial billow occurred with free edge length $\geq$1.7d. Conclusions: Free edge length $\geq$1.5d was required to avoid aortic stenosis in simulations. Cases with free edge length $\geq$1.7d showed excessive billow and other changes in gross morphology. Cases with free edge length 1.5-1.6d have a total free edge length approximately equal to the annular circumference and appeared optimal. These effects should be studied in vitro and in animal studies.

q-bio.TO↗