Coronary Flow Modeling for Contrast Injection and Pullback Timing
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Solution Overview
Problem
Existing in-vitro and virtual angiogram methods for vascular imaging fail to accurately simulate the viscosity and velocity differences between blood and contrast agents, leading to inaccurate results and inefficient clinical procedures.
Innovation Solution
A computational fluid dynamics model is used to generate patient-specific virtual models of coronary arteries, simulating the flow of fluids with distinct viscosities and velocities to optimize contrast agent volume, injection force, and tool pullback times for improved intravascular imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the virtual-ink method is used to determine contrast-agent concentration, then the imaging procedure can be planned, but the accuracy is compromised because the method assumes equivalent viscosities for blood and contrast agent while in reality contrast agent viscosity is 23% to 530% higher than blood viscosity
Solution Approach 1:
The patent applies parameter changes by modifying the viscosity parameter in the computational fluid dynamics model to reflect the actual viscosity difference between contrast agent and blood. Instead of assuming equal viscosities, the model now incorporates the real viscosity ratio (23% to 530% higher for contrast agent), thereby improving the accuracy of contrast-agent concentration determination while maintaining adaptability to real clinical conditions.
2Measurement precision
If the virtual-ink method is used to simulate contrast agent flow, then the imaging parameters can be determined, but the accuracy is reduced because the method assumes contrast agent advection occurs at the same velocity as blood while in reality contrast agent velocity can be significantly different
Solution Approach 1:
The patent modifies the velocity parameter in the computational fluid dynamics model to account for the actual velocity difference between contrast agent and blood. The model now incorporates distinct velocity profiles for each fluid based on their respective viscosities and flow characteristics, improving measurement precision without excessively increasing model complexity through systematic parameter adjustment.
3Measurement precision
If in-vitro bench testing with rigid polymeric coronary artery models is performed, then the positioning of stent can be visualized, but the testing becomes costly and labor intensive while the models do not directly mimic human anatomy
Solution Approach 1:
The patent creates a virtual copy of the coronary artery anatomy using computational fluid dynamics modeling, eliminating the need for physical in-vitro models. This virtual model replicates the anatomical structure and fluid dynamics characteristics, providing accurate stent positioning visualization while significantly improving productivity by reducing costs and labor intensity associated with physical model fabrication and testing.
4Measurement precision
If contrast agent volume is increased to improve imaging quality, then the visualization accuracy improves, but the risk of adverse reactions increases
Solution Approach 1:
The patent implements a feedback mechanism through computational fluid dynamics simulation that predicts the optimal contrast agent volume required for quality imaging. By modeling the actual fluid dynamics and viscosity characteristics, the system determines the minimum effective contrast volume needed, providing feedback to guide contrast administration and thereby improving imaging quality while minimizing adverse reaction risk through reduced contrast exposure.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides more accurate parameters for intravascular imaging, reducing the risk of adverse reactions and streamlining procedures by determining optimal contrast agent use and tool pullback times.
Implementation Method 1
simulating, using a computational fluid dynamics model using the first viscosity value and the second viscosity value, a flow of the first fluid and the second fluid through the virtual model
Implementation Method 2
the first fluid has a first velocity and a first viscosity value, wherein the second fluid has a second velocity profile and a second viscosity value
Implementation Method 3
the virtual-ink method assumes that advection of the contrast agent occurs at the same velocity as that for the blood
Data Source
AI summary
Systems and methods described herein relate to developing a virtual anatomical model of a coronary artery with specific conditions and related fluid properties, such as the velocity and viscosity of selected fluid, to provide accurate parameters for an intravascular imaging procedure. The resulting parameters may relate to the contrast agent volume, time for pullback of intravascular tool, and contrast agent injection force. The models may be patient specific, based on characteristics of a patient's artery. The systems and methods may be utilized for PCI planning, vascular device design and process optimization.