Vascular Tree Generation Using Joint Prior Information
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Solution Overview
Problem
Current methods for assessing coronary artery disease, such as perfusion scans, are costly and expose patients to unnecessary radiation, while conventional anatomical scans lack the resolution to accurately reconstruct microvascular tree networks, leading to inadequate assessment of cardiovascular disease.
Innovation Solution
The system and method utilize cardiac perfusion data as prior information to simulate microvascular networks, allowing for the generation of anatomically and physiologically plausible models of vascular trees, including microvascular networks, to simulate tissue perfusion under various conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If perfusion scans are used to assess tissue perfusion, then measurement precision is improved, but cost increases and patients are exposed to radiation
Solution Approach 1:
The patent creates a virtual copy of the microvascular network through simulation, replacing the need for physical perfusion scans. The simulated vascular tree model replicates the functional behavior of actual microvasculature, allowing perfusion assessment without radiation exposure while maintaining measurement precision
Solution Approach 2:
The patent introduces a computational simulation model as an intermediary between anatomical scans and perfusion assessment. This mediator translates structural anatomical data into functional perfusion information without requiring direct radiation-based imaging, thus eliminating harmful radiation while preserving measurement accuracy
2Device complexity
If conventional anatomical scans are used, then device complexity is reduced, but manufacturing precision is worsened due to insufficient resolution for microvascular reconstruction
Solution Approach 1:
The patent transforms the resolution parameter from the imaging modality to the simulation output. While input anatomical scans use conventional resolution, the simulation process generates high-resolution microvascular structures by extrapolating from visible vessels, effectively changing the resolution parameter at the output stage without requiring high-resolution input imaging
Solution Approach 2:
The patent adds a computational simulation dimension to the traditional imaging process. By moving from direct imaging to indirect simulation, the system achieves microvascular resolution through mathematical modeling and physiological constraints rather than through improved imaging hardware, effectively adding a new dimensional approach to the problem
3Measurement precision
If invasive assessments are used to assess treatment, then measurement precision is improved, but ease of operation is worsened
Solution Approach 1:
The patent replaces invasive physical measurements with a virtual simulation model. The simulated vascular tree serves as a digital twin that provides the same diagnostic information as invasive assessments but without the procedural complexity and risks associated with catheter-based measurements
Solution Approach 2:
The patent substitutes mechanical invasive measurement systems with computational simulation. Instead of physically navigating catheters through vessels to measure pressure gradients, the system uses computational fluid dynamics on a simulated vascular model, replacing complex mechanical procedures with software-based analysis that is easier to perform
Data Source
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AI summary
Systems and methods are disclosed for using cardiac perfusion data as prior information for simulating microvascular networks to guide diagnosis or treatment for cardiovascular disease. One method includes: receiving a patient-specific coronary model of a patient anatomy; receiving a patient-specific model of a target tissue in which a blood flow may be estimated; receiving patient-specific blood perfusion characteristics of the target tissue; determining, using a processor, an association between coronary arteries of the patient-specific coronary model and the perfusion characteristics of the target tissue using joint prior information from the patient-specific coronary model and the perfusion data; generating a complete vascular tree model extending to a microvascular scale by applying joint prior information from the coronary model and the blood perfusion characteristics of the target tissue; simulating a microvascular tree network extending from the coronary arteries to the associated perfusion regions of the target tissue; and outputting the complete 3D model of the coronary system with the extended microvascular tree network.