Mitral Valve Leaflet Stress Analysis via 3D Ultrasound Segmentation
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Solution Overview
Problem
Current methods for analyzing heart valves rely on manual tracing and generic assumptions about leaflet thickness, which are inadequate for accurate stress analysis, especially in mitral valve disease, where leaflet thickness significantly influences biomechanical simulations.
Innovation Solution
A semi-automated approach using 3D ultrasound images to create anatomically accurate, patient-specific models of mitral valves with locally varying thickness, applied to finite element analysis (FEA) for estimating stress distributions on mitral leaflets, incorporating user-initialized segmentation and 3D continuous medial representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracing is used to reconstruct valve geometry from 3D echocardiographic image data, then the process is simple and straightforward, but the measurement precision and manufacturing precision are insufficient for accurate stress analysis
Solution Approach 1:
The patent introduces an intermediary automated segmentation algorithm that acts as a mediator between the raw 3D echocardiographic images and the final geometric models. This algorithm processes the images to extract valve geometry and leaflet thickness measurements automatically, eliminating the need for manual tracing while providing superior measurement precision. The intermediary system handles the complexity of image processing, allowing users to obtain accurate results without performing complex manual operations.
Solution Approach 2:
The patent replaces the mechanical manual tracing process with an automated computational system. Instead of manually outlining valve structures in 3D images, the system uses algorithms to automatically segment the valve geometry and calculate leaflet thickness. This substitution of mechanical manual operations with automated computational methods significantly improves measurement precision while maintaining ease of use.
2Reliability
If generic assumptions of uniform leaflet thickness are used in FEA studies, then the modeling process is simplified, but the reliability of stress analysis is compromised
Solution Approach 1:
The patent applies local quality by transitioning from uniform thickness assumptions to location-specific thickness measurements. The automated segmentation algorithm extracts actual leaflet thickness values at different locations across the valve surface, allowing the FEA model to incorporate spatially varying thickness properties. This local customization of thickness data significantly improves the reliability of stress analysis by reflecting the true anatomical variations in leaflet thickness.
Solution Approach 2:
The patent performs preliminary action by pre-processing the 3D echocardiographic images to extract and map leaflet thickness measurements before conducting the FEA analysis. The automated segmentation and thickness calculation are performed in advance, creating a ready-to-use geometric model with accurate thickness data. This preliminary processing eliminates the need for manual thickness measurements during the FEA setup and ensures consistent, reliable results.
3Adaptability or versatility
If ex vivo porcine valve tissue data is used for thickness assumptions, then the data is readily available, but the adaptability to in vivo human valve tissue is limited
Solution Approach 1:
The patent enables the system to serve itself by using the patient's own 3D echocardiographic images to extract their specific valve geometry and thickness characteristics. Instead of relying on generic data from ex vivo porcine tissue, the automated algorithm processes the in vivo human images directly to obtain patient-specific measurements. This self-service approach eliminates the need for external data sources and ensures the model accurately represents the actual patient anatomy.
Solution Approach 2:
The patent changes the key parameter from generic ex vivo thickness data to in vivo measured thickness parameters specific to each patient. By using automated segmentation of 3D echocardiographic images, the system extracts actual thickness values from living human patients, capturing the true in vivo conditions including tissue hydration, pressure, and anatomical variations. This parameter transformation from generic to patient-specific data dramatically improves adaptability to human mitral valve analysis.
Data Source
AI summary
A method is provided for measuring or estimating stress distributions on heart valve leaflets by obtaining three-dimensional images of the heart valve leaflets, segmenting the heart valve leaflets in the three-dimensional images by capturing locally varying thicknesses of the heart valve leaflets in three-dimensional image data to generate an image-derived patient-specific model of the heart valve leaflets, and applying the image-derived patient-specific model of the heart valve leaflets to a finite element analysis (FEA) algorithm to estimate stresses on the heart valve leaflets. The images of the heart valve leaflets may be obtained using real-time 3D transesophageal echocardiography (rt-3DTEE). Volumetric images of the mitral valve at mid-systole may be analyzed by user-initialized segmentation and 3D deformable modeling with continuous medial representation to obtain, a compact representation of shape. The regional leaflet stress distributions may be predicted in normal and diseased (regurgitant) mitral valves using the techniques of the invention.