Jeffrey A. Feinstein

Bohan J. Li, Nicholas C. Dorn, Andras Lasso et al.

Stenting is among the most common transcatheter interventions for congenital heart disease (CHD). Patient-specific computational fluid dynamics (CFD) simulations can predict hemodynamic outcomes of intervention scenarios but require post-operative vascular geometries that reflect stent-induced shape changes, which existing tools either model inadequately or require extensive time or manual effort to generate. We present SDFStent, a signed distance function (SDF) based mesh deformation method for virtual stenting that operates in real time, maintains mesh integrity, and preserves junction geometry. The stent is modeled as a pipe surface composed of piecewise-capsule SDFs joined by a smooth-minimum operator. Mesh vertices near the expanding SDF surface are displaced along the SDF gradient with a compactly supported fall-off function and an alpha blending mask. SDFStent was benchmarked against three existing approaches and validated on three tetralogy of Fallot (ToF) patients and three coarctation of the aorta (CoA) patients using rigid-wall steady-state CFD simulations against clinical catheterization measurements. Against a prescribed diameter of 6.0 mm, the method produced a mean stented diameter of 5.92 $\pm$ 0.08 mm in 1.5 s, over 100$\times$ faster than the best stenting-specific comparator. All output meshes were watertight and self-intersection-free. CFD-simulated post-operative pressure drops agreed with clinical measurements within 4 mmHg (mean error 2 mmHg). SDFStent produces simulation-ready post-stent models that match prescribed stent dimensions at interactive speeds, from pre-operative anatomy and catheterization data alone. The implementation is open-source and available in 3D Slicer. Its scriptable architecture enables automated generation of large synthetic cohorts for data-driven surrogate modeling.

Bozhi Sun, Seda Tierney, Jeffrey A. Feinstein et al.

Scheduling echocardiographic exams in a hospital presents significant challenges due to non-deterministic factors (e.g., patient no-shows, patient arrival times, diverse exam durations, etc.) and asymmetric resource constraints between fetal and non-fetal patient streams. To address these challenges, we first conducted extensive pre-processing on one week of operational data from the Echo Laboratory at Stanford University's Lucile Packard Children's Hospital, to estimate patient no-show probabilities and derive empirical distributions of arrival times and exam durations. Based on these inputs, we developed a discrete-event stochastic simulation model using SimPy, and integrate it with the open source Gymnasium Python library. As a baseline for policy optimization, we developed a comparative framework to evaluate on-the-fly versus reservation-based allocation strategies, in which different proportions of resources are reserved in advance. Considering a hospital configuration with a 1:6 ratio of fetal to non-fetal rooms and a 4:2 ratio of fetal to non-fetal sonographers, we show that on-the-fly allocation generally yields better performance, more effectively adapting to patient variability and resource constraints. Building on this foundation, we apply reinforcement learning (RL) to derive an approximated optimal dynamic allocation policy. This RL-based policy is benchmarked against the best-performing rule-based strategies, allowing us to quantify their differences and provide actionable insights for improving echo lab efficiency through intelligent, data-driven resource management.