Stenosis Significance Assessment via 3D CFD Simulation
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Solution Overview
Problem
Current methods for assessing the hemodynamic significance of coronary stenosis, such as cardiac stress tests and fractional flow reserve measurements, have limitations including contraindications, invasive procedures, and inaccuracies due to non-linear relationships and external factors, leading to overlooked intermediate stenosis cases.
Innovation Solution
A computer-implemented system using a pre-trained reasoning module and 3D image segmenter, based on convolutional neural networks, processes dimensional and blood flow parameters to determine stenosis significance without invasive clinical examinations, providing a stenosis significance parameter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If FFR measurement is performed to assess stenosis significance, then hemodynamic significance can be evaluated, but the procedure is invasive and requires maximum coronary flow that cannot be completely achieved by adenosine administration
Solution Approach 1:
The patent replaces the mechanical/invasive FFR measurement system with a computational fluid dynamics simulation system. The system uses 3D reconstructed models of coronary arteries and numerical simulations to calculate pressure drops and flow characteristics, eliminating the need for invasive catheter-based pressure measurements while maintaining assessment accuracy.
Solution Approach 2:
The patent creates a virtual copy of the coronary artery system through 3D reconstruction from medical images. This digital twin allows for non-invasive simulation of blood flow and pressure conditions, replicating the functional assessment that would otherwise require invasive FFR measurement in the actual patient anatomy.
2Measurement precision
If FFR measurement is used to evaluate stenosis, then hemodynamic significance can be determined, but discordance with CFR and dependence on aortic pressure and vessel diameter affect accuracy
Solution Approach 1:
The patent transforms the assessment from relying on a single pressure-based parameter (FFR) to a multi-parameter analysis including pressure drops, flow rates, energy losses, and geometric characteristics. This comprehensive parameter set provides a more reliable and consistent evaluation that is not dependent on aortic pressure variations or vessel diameter alone.
Solution Approach 2:
The patent combines multiple assessment dimensions (geometric parameters from 3D reconstruction, hemodynamic parameters from CFD simulation, and energy loss calculations) into a composite evaluation framework. This multi-faceted approach integrates structural and functional information to provide a more reliable stenosis significance assessment.
3Device complexity
If 2D images are used for stenosis assessment, then imaging is simpler, but spatial shape of plaque, location and flow disturbances are completely neglected
Solution Approach 1:
The patent transitions from 2D image analysis to 3D reconstruction of coronary artery geometry. This dimensional enhancement allows for accurate representation of plaque spatial shape, precise location identification, and comprehensive flow disturbance analysis that are impossible with 2D imaging alone.
Solution Approach 2:
The patent introduces 3D reconstruction and CFD simulation as intermediary processes between raw medical images and stenosis assessment. These intermediaries transform static 2D images into dynamic 3D models with hemodynamic properties, enabling comprehensive evaluation of spatial shape and flow characteristics.
4Measurement precision
If exercise stress test is performed to assess heart function, then response to increased oxygen demand can be evaluated, but numerous contraindications prevent its performance
Solution Approach 1:
The patent replaces the physiological stress test mechanism with a computational simulation approach. Instead of physically stressing the patient's heart through exercise, the system uses CFD simulations to model blood flow under various conditions, providing heart function assessment without subjecting patients to exercise-related contraindications.
Data Source
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AI summary
A method for determining a significance of a stenosis in a currently examined blood vessel, the method comprising: providing a pre-trained reasoning module (130) that has been trained to output a value of a stenosis significance parameter by means of a training data set comprising a plurality of records of prior clinically examined stenosis cases, each training record comprising data related to dimensional parameters, blood flow parameters and clinical measurement parameters of the prior clinically examined blood vessel containing the stenosis; inputting, to the pre-trained reasoning module (130), an examination record comprising data related to the dimensional parameters of the currently examined blood vessel containing the stenosis and instructing the reasoning module (130) to output the value of the stenosis significance parameter based on the examination record.