# A multi-physics simulator for pediatric cardiac surgical planning

> **NIH NIH R01** · STANFORD UNIVERSITY · 2024 · $771,838

## Abstract

PROJECT SUMMARY
 Congenital heart disease (CHD) affects one in one hundred babies and is the leading cause of infant mortality
in the US1. Single ventricle (SV) physiology is among the most complex and high-risk CHD diagnoses, in which
patients are born with only one functional pumping chamber in the heart. These patients are typically palliated
with three open heart surgeries culminating with a Fontan procedure, subjecting patients to a lifetime of elevated
venous pressures and high rates of morbidity and mortality. As many as half of Fontan patients degenerate
into heart failure and required a transplant by the age of 25. However, for a subset of patients with borderline
SV physiology, drastic improvements in outcomes can be achieved if a bi-ventricular circulation can be restored.
Despite these advantages, current reconstruction procedures require “on the fly” surgical planning in which the
surgeon must customize a baffle design for each individual patient in the operating room. Computational modeling
is well positioned to address these needs by providing surgical teams with predictive simulations. However,
current simulation capabilities are hindered by several key factors: 1) anatomic models are laborious to construct,
2) there is a current lack of data characterizing CHD hearts, including material properties, fiber orientations,
and Purkinje system structure and these quantities are critically needed for accurate simulations, and 3) current
solvers do not combine all the relevant physics for whole-heart simulations. We aim to address these needs by
developing a pediatric cardiac simulator to support surgical planning in complex congenital heart disease
and to deploy it in a prospective clinical study. To accomplish these goals, we propose the following three
specific aims: 1) To Enable Rapid Patient Specific Model Construction of Topologically Unique CHD Hearts With
Machine Learning Methods Based On Signed Distance Fields. 2) To Characterize Mechanics and Microstructure
in CHD Hearts Using Ex Vivo MR Acquisition and Finite-element Based Inverse Modeling. 3) To Prospectively
Demonstrate and Validate a Novel Multi-Physics Cardiac Solver for Biventricular Reconstructive Surgical Planning
in CHD Patients. Our proposed study will tightly integrate image processing and machine learning, advanced
experimental magnetic resonance image acquisition methods, and development of state-of-the-art multi-physics
finite element solvers (combining fluid and solid mechanics, active contraction, valves, and electrophysiology)
to address an immediate clinical need in a high-risk and understudied patient population. A primary goal is to
demonstrate improvements in short-term clinical outcomes before the end of the project. This proposal brings
together an interdisciplinary team comprising experts in computational modeling of cardiovascular biomechanics,
advanced MRI methods, microstructural and mechanical tissue characterization, and pediatric cardiac surgery.
O...

## Key facts

- **NIH application ID:** 10869792
- **Project number:** 1R01HL173845-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Daniel B Ennis
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $771,838
- **Award type:** 1
- **Project period:** 2024-05-03 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10869792

## Citation

> US National Institutes of Health, RePORTER application 10869792, A multi-physics simulator for pediatric cardiac surgical planning (1R01HL173845-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10869792. Licensed CC0.

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