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Biomechanically Informed Image Registration for Patient-Specific Aortic Valve Strain Analysis

Mohsen Nakhaei, Alison Pouch, Silvani Amin, Matthew Daemer, Christian Herz, Natalie Yushkevich, Lourdes Al Ghofaily, Nimesh Desai, Joseph Bavaria, Matthew Jolley, Wensi Wu

TL;DR

This study tackles the challenge of obtaining patient-specific aortic valve strain information from imaging by marrying biomechanically informed finite-element simulations with deformable image registration. By generating intermediate valve configurations (a synthetic closed state) from patient-specific geometries derived from 4D TEE and CT data, and using these frames to guide registration, the approach achieves robust full-cycle tracking and reliable leaflet strain estimates, reducing dependency on uncertain boundary conditions and material properties. Across 20 patients, the method yields a 40% improvement in registration accuracy and reveals distinct deformation patterns among trileaflet adults, bicuspid adults, and pediatric valves, with areal, Green-Lagrange, and effective strain maps highlighting regions at risk for remodeling or failure. The framework thus enables personalized assessment of valve mechanics with potential to inform tailored repair strategies and improve long-term outcomes.

Abstract

Aortic valve (AV) biomechanics play a critical role in maintaining normal cardiac function. Pathological variations, particularly in bicuspid aortic valves (BAVs), alter leaflet loading, increase strain, and accelerate disease progression. Accurate, patient-specific characterization of valve geometry and deformation is essential for predicting disease progression and guiding durable repair. Current imaging and computational methods often fail to capture rapid valve motion and complex patient-specific features. To address these challenges, we combined image registration with finite element method (FEM) to enhance AV tracking and biomechanical assessment. Patient-specific valve geometries from 4D transesophageal echocardiography (TEE) and CT were used in FEM to model AV closure and generate intermediate deformation states. The FEM-generated states facilitated leaflet tracking, while the registration algorithm corrected mismatches between simulation and image. Across 20 patients, FEM-augmented registration improved accuracy by 40% compared with direct registration (33% for TEE, 46% for CT). This improvement enabled more reliable strain estimation directly from imaging and reducing uncertainties from boundary conditions and material assumptions. Areal and Green-Lagrange strains, as well as effective strain, were quantified in adult trileaflet/bicuspid, and pediatric patients. Trileaflet adults showed uniform deformation, BAVs exhibited asymmetric strain, and pediatric valves had low mean areal strain with high variability. Convergence between trileaflet adult and pediatric valves in mean effective strain suggests volumetric deformation drives age- and size-related differences. The FEM-augmented registration framework enhances geometric tracking and provides clinically relevant insights into patient-specific AV deformation, supporting individualized intervention planning.

Biomechanically Informed Image Registration for Patient-Specific Aortic Valve Strain Analysis

TL;DR

This study tackles the challenge of obtaining patient-specific aortic valve strain information from imaging by marrying biomechanically informed finite-element simulations with deformable image registration. By generating intermediate valve configurations (a synthetic closed state) from patient-specific geometries derived from 4D TEE and CT data, and using these frames to guide registration, the approach achieves robust full-cycle tracking and reliable leaflet strain estimates, reducing dependency on uncertain boundary conditions and material properties. Across 20 patients, the method yields a 40% improvement in registration accuracy and reveals distinct deformation patterns among trileaflet adults, bicuspid adults, and pediatric valves, with areal, Green-Lagrange, and effective strain maps highlighting regions at risk for remodeling or failure. The framework thus enables personalized assessment of valve mechanics with potential to inform tailored repair strategies and improve long-term outcomes.

Abstract

Aortic valve (AV) biomechanics play a critical role in maintaining normal cardiac function. Pathological variations, particularly in bicuspid aortic valves (BAVs), alter leaflet loading, increase strain, and accelerate disease progression. Accurate, patient-specific characterization of valve geometry and deformation is essential for predicting disease progression and guiding durable repair. Current imaging and computational methods often fail to capture rapid valve motion and complex patient-specific features. To address these challenges, we combined image registration with finite element method (FEM) to enhance AV tracking and biomechanical assessment. Patient-specific valve geometries from 4D transesophageal echocardiography (TEE) and CT were used in FEM to model AV closure and generate intermediate deformation states. The FEM-generated states facilitated leaflet tracking, while the registration algorithm corrected mismatches between simulation and image. Across 20 patients, FEM-augmented registration improved accuracy by 40% compared with direct registration (33% for TEE, 46% for CT). This improvement enabled more reliable strain estimation directly from imaging and reducing uncertainties from boundary conditions and material assumptions. Areal and Green-Lagrange strains, as well as effective strain, were quantified in adult trileaflet/bicuspid, and pediatric patients. Trileaflet adults showed uniform deformation, BAVs exhibited asymmetric strain, and pediatric valves had low mean areal strain with high variability. Convergence between trileaflet adult and pediatric valves in mean effective strain suggests volumetric deformation drives age- and size-related differences. The FEM-augmented registration framework enhances geometric tracking and provides clinically relevant insights into patient-specific AV deformation, supporting individualized intervention planning.
Paper Structure (21 sections, 10 equations, 7 figures, 1 table)

This paper contains 21 sections, 10 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Model preparation from clinical images
  4. Segmentation
  5. Medial surface extraction
  6. Finite element valve model
  7. Constitutive model
  8. Boundary conditions and loading
  9. Simulation and model convergence
  10. Image registration
  11. Strain calculation
  12. Results
  13. 4D echocardiography images
  14. Trileaflet aortic valves
  15. Bicuspid aortic valves
  16. ...and 6 more sections

Figures (7)

  • Figure 1: Registration pipeline with binary images from FE simulation.
  • Figure 2: Finite element model development. (A) Extraction of medial surface of the valve leaflets at mid-systole. (B) Definition of boundary conditions for the annulus and the free edge of the leaflet. (D) Estimation of annulus displacement. (C) Imposed uniform diastolic pressure on the leaflets.
  • Figure 3: Example of image-based registration results for one TEE case and one CT case. The reconstructed aortic valve leaflets at mid-diastole are shown in red. (A) TEE with direct registration, (B) TEE with FEM-augmented registration, (C) CT with direct registration, and (D) CT with FEM-augmented registration.
  • Figure 4: Registration and strain results for trileaflet adult aortic valves from 4D TEE images. Examples with lower (TAV-A/B/C) and higher (TAV-D) mean distance values after valve registration from open to closed configuration, computed between registered and ground-truth manual segmentations. L: left coronary leaflet; R: right coronary leaflet; N: non-coronary leaflet.
  • Figure 5: Registration and strain results on bicuspid adult aortic valves 4D TEE images, showing valve registration from open to closed configuration. L: left coronary leaflet; R: right coronary leaflet; N: non-coronary leaflet.
  • ...and 2 more figures