# Data-Driven Automation of Patient-Specific Finite Element Modeling for TAVR

> **NIH NIH F31** · YALE UNIVERSITY · 2022 · $46,752

## Abstract

PROJECT SUMMARY/ABSTRACT
Transcatheter Aortic Valve Replacement (TAVR) is an emerging treatment option for aortic stenosis, a
common heart valve disease that causes about 15,000 deaths per year in the U.S. TAVR has been steadily
gaining popularity since 2011, and is now performed over 70,000 times per year in the U.S. Finite element (FE)
methods have shown great potential for improving TAVR treatment planning by simulating the biomechanical
interactions between anatomical structures and deployed prosthetic devices. However, FE methods are
currently severely limited by the delineation process of patient-specific geometry, as manual delineation from
3D CT images is extremely time consuming and error-prone. Automated methods have been proposed, but
they have limited adaptability due to extensive assumptions about input and output characteristics. This is
especially problematic when extensions of patient-specific geometry are required to simulate various
complications of TAVR. To address these limitations, this proposal aims to develop fast, robust, and easily
adaptable deep learning algorithms for automating the delineation of patient-specific geometry from 3D CT
images. Aim 1 is to develop template deformation-based weakly supervised deep learning algorithms to
delineate TAVR-relevant anatomical structures such as the upper left ventricular myocardium, aortic valve,
coronary arteries, and ascending aorta. The template deformation strategy will establish mesh correspondence
between all predicted volumetric FE outputs, and weak supervision will allow for modeling of the complex
output geometry with minimally sufficient expert labeling. Aim 2 is to incorporate anatomically consistent
calcification to the final mesh outputs using multi-task deep learning. Based on prior medical knowledge
that calcification should always be in close proximity to anatomical surfaces, the main goal for Aim 2 is to
encourage effective sharing of imaging features from Aim 1 to also locate calcification. A novel loss for
anatomical consistency will also be developed as part of this aim. Upon successful completion of this proposal,
the final unified deep learning model will be able to use pre-operative 3D CT images to generate fully functional
patient-specific volumetric FE meshes for accurate and versatile TAVR simulations, at a rate of ~20ms per
image. This is a speed-up of several orders of magnitude compared to the current workflow, and thus will
significantly accelerate biomechanics studies and bring FE simulations closer to clinical use. This work will be
conducted at Yale University’s Biomedical Engineering department with guidance from Dr. James Duncan and
Dr. Wei Sun under the F31 fellowship. The training will include extensive research at the intersection of
biomedical image analysis, biomechanics, and machine learning, with emphasis on impactful clinical
applications.

## Key facts

- **NIH application ID:** 10386122
- **Project number:** 1F31HL162505-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Daniel Pak
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $46,752
- **Award type:** 1
- **Project period:** 2022-03-01 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10386122

## Citation

> US National Institutes of Health, RePORTER application 10386122, Data-Driven Automation of Patient-Specific Finite Element Modeling for TAVR (1F31HL162505-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10386122. Licensed CC0.

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