Iterative Contrast Distribution Simulation for Coronary Stenosis Assessment
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Solution Overview
Problem
Current non-invasive methods for assessing coronary artery blood flow and fractional flow reserve (FFR) are limited by inaccurate estimation of coronary boundary conditions, which affects the accuracy of blood flow quantification and pressure waveforms, particularly in patients with coronary artery stenoses where flow and area changes significantly.
Innovation Solution
The method involves receiving patient-specific images and contrast distribution data, associating them with a patient-specific anatomic model, defining and updating physiological and boundary conditions to simulate contrast agent distribution, and calculating blood flow characteristics until a similarity condition is met, thereby improving the estimation of blood flow and pressure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-invasive methods are used to assess coronary artery blood flow and FFR, then patient comfort and reduced invasiveness are improved, but accuracy of blood flow quantification and pressure waveform estimation deteriorates due to inaccurate boundary conditions
Solution Approach 1:
The system uses measured contrast distribution data from CT imaging as feedback to iteratively update and refine the estimated boundary conditions in the computational model. This feedback loop allows the non-invasive method to improve its accuracy by comparing simulated contrast distribution with actual measured distribution and adjusting boundary conditions accordingly, resolving the contradiction between non-invasive operation and measurement precision.
Solution Approach 2:
The patent introduces contrast distribution measurements as an intermediary element that bridges the gap between non-invasive imaging and accurate hemodynamic parameters. By using contrast agent distribution as a mediator, the system can derive accurate boundary conditions and blood flow characteristics without direct invasive measurement, thus maintaining ease of operation while improving measurement precision.
2Device complexity
If standard non-invasive modeling is used, then procedural simplicity is maintained, but diagnostic accuracy deteriorates in patients with significant stenoses where flow and area changes significantly
Solution Approach 1:
The system transitions from static boundary condition estimation to dynamic boundary condition refinement by iteratively updating boundary conditions based on measured contrast distribution. This dynamic approach allows the model to adapt to significant flow and area changes in stenotic vessels, improving diagnostic accuracy without requiring excessively complex procedural steps.
Solution Approach 2:
The patent performs preliminary contrast distribution measurement and simulation before final boundary condition determination. By conducting initial simulations and comparing them with measured data, the system prepares refined boundary conditions in advance, enabling accurate diagnostic assessment of stenotic lesions while maintaining reasonable procedural complexity.
3Measurement precision
If iterative boundary condition updating is performed to improve accuracy, then blood flow and pressure estimation precision is improved, but computational time and processing complexity increase
Solution Approach 1:
The system performs iterative boundary condition updates only to the extent necessary to achieve adequate agreement between simulated and measured contrast distribution. By applying partial action (stopping iterations when sufficient accuracy is reached rather than pursuing perfect convergence), the system improves blood flow and pressure estimation precision while limiting excessive computational time consumption.
Data Source
AI summary
Systems and methods are disclosed for assessing the severity of plaque and/or stenotic lesions using contrast distribution predictions and measurements. One method includes: receiving patient-specific images of a patient's vasculature and a measured distribution of a contrast agent delivered through the patient's vasculature; associating the measured distribution of the contrast agent with a patient-specific anatomic model of the patient's vasculature; defining physiological and boundary conditions of a blood flow model of the patient's blood flow and pressure; simulating the distribution of the contrast agent through the patient-specific anatomic model; comparing the measured distribution of the contrast agent and the simulated distribution of the contrast agent through the patient-specific anatomic model to determine whether a similarity condition is satisfied; and updating the defined physiological and boundary conditions and re-simulating distribution of the contrast agent through the one or more points of the patient-specific anatomic model until the similarity condition is satisfied.