Patient-Specific Plaque Progression Modeling from Vascular Images
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Solution Overview
Problem
Current methods for assessing coronary artery disease, such as fractional flow reserve (FFR), are invasive and lack a comprehensive understanding of how plaque geometry impacts blood flow and disease progression, leading to inadequate treatment planning.
Innovation Solution
Systems and methods for predicting plaque progression and regression using computational modeling and machine learning to analyze plaque geometry and its impact on blood flow, generating graphical displays to guide treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional invasive catheterization is used to measure FFR, then measurement accuracy is improved, but patient risk and procedure complexity increase
Solution Approach 1:
The patent creates a virtual copy of the coronary artery geometry from CT scan data and performs computational fluid dynamics simulations on this digital model to calculate FFR values, eliminating the need for physical catheterization while maintaining measurement accuracy
Solution Approach 2:
The patent replaces the mechanical invasive catheterization system with a computational modeling system that uses blood flow physics equations to calculate FFR non-invasively from anatomical images
2Measurement precision
If comprehensive plaque geometry analysis is performed, then treatment planning accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the coronary artery into multiple sections along its length and analyzes plaque characteristics in each segment independently, allowing comprehensive analysis while managing computational complexity through modular processing
Solution Approach 2:
The patent performs preliminary processing of CT scan data to extract centerline geometry and plaque characteristics before running computationally intensive blood flow simulations, preparing the data structure in advance to reduce overall processing time
3Measurement precision
If multiple imaging time points are analyzed, then plaque progression detection accuracy is improved, but patient exposure to radiation and procedure time increase
Solution Approach 1:
The patent creates virtual models of the coronary artery at different time points from CT scan data, allowing longitudinal analysis of plaque progression without requiring repeated invasive procedures or excessive radiation exposure
Data Source
AI summary
Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of a location of disease in the patient's vasculature based on the received patient-specific data; identifying one or more changes in geometry of the anatomic model based on a modeled progression or regression of disease at the location; calculating one or more values of a blood flow characteristic within the patient's vasculature using a computational model based on the identified one or more changes in geometry of the anatomic model; and generating an electronic graphical display of a relationship between the one or more values of the calculated blood flow characteristic and the identified one or more changes in geometry of the anatomic model.