Cardiac Implant Model Determination via Weighted Database Approximation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for building patient-specific models of cardiac implants are computationally heavy and time-consuming, often requiring expert input and taking several hours or days to produce results, and lack accurate pre-operative insights into implant-patient anatomy interactions, leading to potential complications like regurgitation and rupture.
Innovation Solution
A method and system that utilize a database of cardiac implant models and patient images to determine a patient-specific model through a weighted combination of existing records, allowing for efficient, real-time approximation and prediction of the implant's deployment in the cardiac region, using techniques like optimization algorithms and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If finite element modelling and computational fluid dynamics are used to create patient-specific models, then measurement precision and reliability are improved, but computational time and complexity increase significantly
Solution Approach 1:
The patent pre-calculates and stores deformation models for various implant sizes and configurations in a database before actual patient planning. This preliminary computation allows the system to retrieve and combine pre-existing models rather than performing full finite element analysis during the time-critical planning phase, thus maintaining accuracy while dramatically reducing computational time.
Solution Approach 2:
The system creates simplified representative models of patient anatomy from imaging data and uses these copies to select appropriate pre-calculated deformation models from the database. Instead of performing complex simulations on actual patient geometry, the system uses simplified copies to identify the most relevant pre-computed models, which are then combined to predict the final implant deformation.
2Manufacturing precision
If expert input is required for model determination, then manufacturing precision is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The system automatically determines implant deformation models by retrieving data from the database based on patient-specific measurements and combining relevant models through the approximation function. The system performs self-determination without requiring expert intervention, as the approximation function automatically identifies which pre-calculated models to combine and determines their weighting factors based on patient anatomy.
Solution Approach 2:
The database contains a universal set of pre-calculated deformation models that can be applied to various patient anatomies and implant types. The system uses a single approximation function to handle different patient cases by selecting and combining appropriate models from the universal database, eliminating the need for different expert methodologies for different scenarios.
3Ease of operation
If conventional planning tools are used to determine implant size, then ease of operation is improved, but measurement precision and reliability of predicting implant-patient interactions worsen
Solution Approach 1:
The system introduces an intermediary approximation function that bridges simple patient measurements and complex implant deformation predictions. This function takes easily obtained patient-specific measurements as input and retrieves/combines appropriate pre-calculated models from the database to produce accurate predictions of implant-patient interactions, maintaining simplicity while improving precision.
Solution Approach 2:
The system pre-calculates and stores the complex deformation behavior for various implant configurations in the database during the preliminary phase. During actual patient planning, the system only needs to perform simple measurements and retrieve the appropriate pre-calculated models, avoiding the need for complex real-time simulations while maintaining high prediction accuracy.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The invention relates to determining a model of a cardiac implant deployed in a cardiac region of a patient. A database including a plurality of different records is provided, each record including at least data representative of an image of a cardiac region and an associated model of the cardiac implant deployed in said cardiac region. A patient- specific image of the cardiac region for which the model of the cardiac implant deployed in said cardiac region is to be determined, is received. An approximation of the patient-specific image of the cardiac region is calculated based on a weighted combination of images of the cardiac region in a plurality of records. The model of the cardiac implant deployed in the cardiac region associated to the patient-specific image of said cardiac region is determined based on weighted combination of models of the cardiac implant associated to the images of the cardiac region in the plurality of records used for approximating said patient-specific image of the cardiac region.