Coronary Blood Flow Estimation via 4D CT Segmentation
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Solution Overview
Problem
Current methods for determining blood flow through coronary arteries are not sufficiently accurate and reliable, as they neglect the spatial dynamics of coronary arteries due to cardiac motion and do not fully utilize personalized boundary conditions such as blood flow velocity and pressure.
Innovation Solution
A method that combines 3D image data sets of coronary arteries and myocardial muscle using dual-energy or spectral CT scans to derive precise information on blood flow, incorporating segmentation models like the 17-segment model, and performs fluid dynamics simulations to calculate fractional flow reserve and pressure drops across stenoses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluid dynamics calculations are performed based on single phase CT coronary angiography data sets, then additional functional parameter (FFR) is obtained, but spatial dynamics of coronary arteries due to cardiac motion is neglected reducing accuracy
Solution Approach 1:
The patent applies dynamics by transitioning from static single-phase CT imaging to multi-phase 4D CT imaging that captures coronary arteries at multiple time points during the cardiac cycle. This allows the system to account for spatial dynamics and motion of coronary arteries, thereby improving both measurement precision and reliability of blood flow estimation.
Solution Approach 2:
The patent performs preliminary segmentation of coronary arteries from multi-phase CT data sets before conducting fluid dynamics simulations. By pre-processing the imaging data to extract detailed anatomical structures across multiple phases, the system prepares accurate geometric models that incorporate cardiac motion, which then serve as the basis for reliable flow simulations.
2Measurement precision
If personalized boundary conditions like blood flow velocity and pressure are incorporated, then accuracy of flow simulation improves, but complexity of input data requirements increases
Solution Approach 1:
The patent merges anatomical imaging data with physiological measurement data by integrating CT-derived coronary artery geometry with patient-specific boundary conditions (blood flow velocity and pressure measurements). This combination allows the system to perform accurate personalized FFR calculations while managing data complexity through unified processing workflows.
3Measurement precision
If multi-phase 4D CT imaging is used to capture spatial dynamics, then blood flow estimation accuracy improves, but radiation exposure and data processing time increase
Solution Approach 1:
The patent segments the 4D CT data set into multiple phases corresponding to different points in the cardiac cycle. By dividing the complex multi-phase data into manageable segments, the system can process each phase separately through fluid dynamics calculations, thereby reducing overall processing time while maintaining high precision in blood flow velocity estimation across the entire cardiac cycle.
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 a more accurate and reliable estimation of blood flow through coronary arteries, enabling precise calculation of fractional flow reserve and pressure drops, improving diagnostic capabilities for coronary artery disease.
Implementation Method 1
A 3D image data set of at least the coronary arteries and the myocardial muscle and a 3D marker data set indicating the amount of marker contained within voxels of said myocardial muscle are generated from a dual-energy or spectral 3D data set
Data Source
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Figure 3A~3B
AI summary
A method of determining the blood flow through coronary arteries comprises generating (S1) a 3D image data set of at least the coronary arteries and the myocardial muscle, generating (S2) a 3D marker data set of at least the myocardial muscle from a dual- energy or spectral 3D data set obtained after administration of a marker, said 3D marker data set indicating the amount of said marker contained within voxels of said myocardial muscle, subdividing (S3) the myocardial muscle into myocardial muscle segments, determining (S4) which coronary artery supplies the respective myocardial muscle segments, determining (S5) the volume of blood that flows into the respective myocardial muscle segments from said 3D marker data set, and determining (S6) the total volume of blood that flows into a coronary artery of interest by summing the volume of blood flowing into all myocardial muscle segments supplied by said coronary artery.