Coronary Plaque Remodeling Curves for Non-Invasive FFR Planning
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Solution Overview
Problem
Existing methods for diagnosing and treating coronary artery disease lack accurate data on plaque characteristics, geometry, and functional significance, leading to unnecessary invasive treatments and suboptimal treatment planning.
Innovation Solution
Systems and methods for treatment planning based on plaque progression and regression curves, using computational modeling and machine learning to predict how plaque geometry affects hemodynamics, allowing for predictive simulations of plaque remodeling and its impact on blood flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive catheterization is used to measure FFR, then accurate functional significance data is obtained, 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 replica 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 modeling algorithms and coronary CT scan data to calculate FFR non-invasively
2Reliability
If surgical intervention is performed on detected lesions, then treatment is provided, but unnecessary invasive procedures increase when blockages are not functionally significant
Solution Approach 1:
The patent performs preliminary non-invasive FFR calculation and plaque progression/regression analysis before surgical intervention to determine whether lesions are functionally significant, preventing unnecessary procedures by identifying stable plaques that do not require treatment
Solution Approach 2:
The patent changes the diagnostic parameter from anatomical blockage detection alone to functional significance assessment using FFR values and plaque stability metrics, enabling differentiation between lesions requiring treatment and those that are stable
3Measurement precision
If detailed plaque geometry data is collected and analyzed, then treatment planning accuracy is improved, but data processing complexity and time increase
Solution Approach 1:
The patent segments the coronary artery into multiple segments and characterizes plaque in each segment separately, allowing detailed analysis of plaque geometry and composition while managing data complexity through structured organization
Solution Approach 2:
The patent introduces computational modeling algorithms as intermediaries that automatically process complex plaque geometry data from CT scans and generate standardized FFR values and progression/regression curves, reducing manual analysis complexity
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.