Abstract 4355972: Simulation Guided Aortic Valve Tracking and Strain Analysis in 4D Echocardiography

M Mohsen Nakhaei (Children Hospital of Philadelphia, Philadelphia, Pennsylvania, United States) S Silvani Amin (University of Pennsylvania, Philadelphia, Pennsylvania, United States) C Christian Herz (Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States) N Natalie Yushkevich (University of Pennsylvania, Philadelphia, Pennsylvania, United States) L Lourdes Al Ghofaily (University of Pennsylvania, Philadelphia, Pennsylvania, United States) N Nimesh Desai (University of Pennsylvania, Philadelphia, Pennsylvania, United States) J Joseph Bavaria (Jefferson Health, Philadelphia, Pennsylvania, United States) M Matthew Jolley (Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States) A Alison Pouch (University of Pennsylvania, Philadelphia, Pennsylvania, United States) W Wensi Wu (Children Hospital of Philadelphia, Philadelphia, Pennsylvania, United States)

Abstract

Background: Accurate tracking of the aortic valve in 4D transesophageal echocardiography (TEE) and subsequent leaflet strain measurement remain a challenge due to limited imaging temporal resolution. Conventional intensity-based image registration techniques often fail to capture the rapid nonlinear deformation of valve leaflets across cardiac phases. The objective of this work is to present an image analysis framework that temporally augments 4D TEE with finite element modeling (FEM) to reconstruct patient-specific valve motion and quantify leaflet strain in both trileaflet and bicuspid aortic valves. Methods: We propose a hybrid framework that integrates FEM with deformable image registration to achieve leaflet tracking in 4D TEE sequences (Fig 1). First, the aortic valve is manually segmented in a mid-systolic “reference” (open) frame. A shell representation of the segmented leaflets is created and integrated with FEM to simulate valve closure. A final mid-diastolic segmentation of the leaflets is obtained by applying the following transformations to the mid-systolic reference segmentation: (1) the FEM-recovered transformations of valve closure and (2) the registration-derived transformation between an FEM-derived synthetic mid-diastolic image and the real mid-diastolic image. The proposed method was tested on six patients with varying aortic valve abnormalities (Table 1), and leaflet strains were computed. Results: The proposed method significantly improved segmentation tracking accuracy compared to conventional registration. The mean distance between the tracked closed-state segmentation and manual ground truth for six patients was 1.67 ± 0.49 mm using our hybrid approach, versus 3.19 ± 1.17 mm with conventional registration (no FEM). Strain maps showed physiologically consistent patterns with elevated strain near coaptation lines. Notably, patient 4, with severe calcification, exhibited the lowest strain. Representative results are shown in Figure 2. Conclusions: These findings confirm that biomechanically generated intermediate frames enhance registration accuracy and patient-specific geometric fidelity of the aortic valve, enabling strain analysis. The approach has the potential to inform our understanding of diverse valve mechanics and facilitate translational application to the assessment of structural heart disease.

Article Details

Journal Circulation
Volume / Issue Vol. 152, Issue Suppl_3
Published November 04, 2025
ISSN 0009-7322
Publisher Lippincott Williams & Wilkins

Journal Info

Circulation

Lippincott Williams & Wilkins

ISSN: 0009-7322 Health Sciences

Authors (10)

M

Mohsen Nakhaei

Children Hospital of Philadelphia, Philadelphia, Pennsylvania, United States

S

Silvani Amin

University of Pennsylvania, Philadelphia, Pennsylvania, United States

C

Christian Herz

Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States

N

Natalie Yushkevich

University of Pennsylvania, Philadelphia, Pennsylvania, United States

L

Lourdes Al Ghofaily

University of Pennsylvania, Philadelphia, Pennsylvania, United States

N

Nimesh Desai

University of Pennsylvania, Philadelphia, Pennsylvania, United States

J

Joseph Bavaria

Jefferson Health, Philadelphia, Pennsylvania, United States

M

Matthew Jolley

Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States

A

Alison Pouch

University of Pennsylvania, Philadelphia, Pennsylvania, United States

W

Wensi Wu

Children Hospital of Philadelphia, Philadelphia, Pennsylvania, United States