Coronary Stenosis Detection via Pressure Drop Analysis
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Solution Overview
Problem
Current methods for detecting stenosis in vascular systems, such as fractional flow reserve (FFR) measurements, require invasive procedures and rely on geometric calculations, which do not always translate to functional significance, and lack efficient pre-detection of stenosis regions for blood flow modeling and intervention planning.
Innovation Solution
A computer-implemented method and processor-based system that detect stenosis by analyzing segmented image patches of coronary vessels, determining pressure drop and cross-sectional area distributions, and identifying critical points to identify stenosis regions without estimating vessel radius, using both cross-sectional area and pressure drop distributions for accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive FFR measurements are used to detect stenosis, then functional significance of stenosis can be accurately assessed, but the procedure becomes invasive and more complex
Solution Approach 1:
The patent replaces the mechanical/invasive pressure measurement system with a computational fluid dynamics model that uses imaging data to simulate and calculate pressure drops across stenotic lesions. This substitution eliminates the need for invasive wire insertion while providing comparable functional assessment through virtual hemodynamic modeling.
Solution Approach 2:
The patent creates a virtual copy of the coronary vasculature from imaging data and performs hemodynamic simulations on this digital replica. By modeling the vascular geometry and flow characteristics in a computational environment, the system reproduces the functional effects of stenosis without requiring physical intervention in the patient's body.
2Ease of operation
If geometric calculations are used to assess stenosis, then the assessment is simple and non-invasive, but the results do not always translate to functional significance
Solution Approach 1:
The patent transitions from purely geometric parameters (lumen diameter, cross-sectional area) to hemodynamic parameters (pressure drop, flow velocity, fractional flow reserve) by implementing CFD modeling. This parameter transformation allows the system to maintain the non-invasive and simple workflow of imaging-based assessment while capturing the functional significance that geometric measurements alone cannot provide.
3Productivity
If a priori detection of stenosis regions is performed using reduced-order models, then computational efficiency is improved, but detection accuracy may be compromised
Solution Approach 1:
The patent segments the coronary vasculature into distinct vessel segments and applies reduced-order CFD models to each segment individually. By dividing the complex vascular tree into manageable sections, the system achieves computational efficiency while maintaining adequate detection accuracy for clinical decision-making. Critical stenosis regions are identified through systematic analysis of pressure drops across each segmented region.
Data Source
AI summary
A computer-implemented method includes obtaining, via a processor, segmented image patches of a vessel along a coronary tree path and associated coronary flow distribution for respective vessel segments in the segmented image patches. The method also includes determining, via the processor, a pressure drop distribution along an axial length of the vessel from the segmented image patches and the associated coronary flow distribution. The method further includes determining, via the processor, critical points in the pressure drop distribution. The method even further includes detecting, via the processor, a presence of a stenosis based on the critical points in the pressure drop distribution.