Occluded Artery Simulation for Blood Flow Redistribution Planning
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Solution Overview
Problem
Occlusion-based treatments for occlusive diseases do not adequately account for the redistribution of blood flow, leading to uncertainties in treatment efficacy, as the impact on patient-specific hemodynamic parameters is not evaluated prior to treatment.
Innovation Solution
A system and method for simulating occluded arteries and optimizing occlusion-based treatments by generating patient-specific computational models to predict blood flow changes before and after treatment, using computational methods and machine learning to model hemodynamic impacts, and optimizing treatment parameters through cost functions and optimization algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If occlusion-based treatments are performed without pre-treatment simulation, then treatment can be performed quickly, but the impact on blood flow redistribution and treatment efficacy cannot be evaluated
Solution Approach 1:
The system performs preliminary computational simulation of blood flow redistribution before the actual occlusion-based treatment is administered. By creating virtual models and predicting hemodynamic changes in advance, the system enables treatment planning that informs clinical decisions without delaying the actual treatment procedure, thus resolving the contradiction between quick treatment execution and comprehensive evaluation of blood flow impacts
2Reliability
If computational models are used to simulate blood flow changes, then treatment efficacy can be evaluated, but the complexity of the treatment planning process increases
Solution Approach 1:
The system replaces complex physical measurement and evaluation methods with computational modeling and simulation. By using software-based virtual hemodynamic analysis instead of requiring complex physical measurement devices and manual calculations, the system achieves reliable treatment efficacy evaluation while managing computational complexity through automated algorithms and user-friendly interfaces
3Measurement precision
If post-treatment computational models are generated to predict blood flow changes, then accurate perfusion prediction is achieved, but additional computational resources and processing time are required
Solution Approach 1:
The system optimizes the balance between prediction accuracy and computational resource consumption by adjusting simulation parameters, using simplified hemodynamic models when appropriate, and applying adaptive computational methods that allocate resources based on the specific clinical scenario and required precision level, thus achieving accurate perfusion prediction without excessive computational overhead
Data Source
AI summary
Systems and methods are disclosed for simulation of occluded arteries and/or optimization of occlusion-based treatments. One method includes obtaining a patient-specific anatomic model of a patient's vasculature; obtaining an initial computational model of blood flow through the patient's vasculature based on the patient-specific anatomic model; obtaining a post-treatment computational model by modifying portions of the initial computational model based on an occlusion-based treatment; generating a pre-treatment blood flow characteristic using the initial computational model or computing a post-treatment blood flow using the post-treatment computational model; and outputting a representation of the pre-treatment blood flow characteristic or the post-treatment blood flow characteristic.