Subject-Specific CFD Model for Cardiovascular Diagnosis
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Solution Overview
Problem
Current methods for simulating blood flow and structural features in the cardiovascular system lack accuracy and clinical feasibility, particularly in predicting the outcome of cardiovascular surgeries, due to the complexity of the heart's anatomy and physiology, and the need for subject-specific models that can account for time-varying geometries and interactions between blood flow and heart tissue.
Innovation Solution
A subject-specific computational fluid dynamics (CFD) model is developed using medical imaging data, such as echocardiography, to create transient geometry models that simulate blood flow and structural features, incorporating Fluid Structure Interaction (FSI) algorithms and machine learning for optimizing treatment planning and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subject-specific computational models are developed to improve diagnostic precision and treatment planning, then measurement precision and reliability improve, but device complexity and computational requirements increase
Solution Approach 1:
The computational model is divided into discrete computational elements (finite elements or finite volumes) that can be individually processed. This segmentation allows complex cardiovascular geometries to be broken down into manageable units, enabling subject-specific modeling without overwhelming computational complexity.
Solution Approach 2:
The model incorporates time-varying geometries that dynamically adapt to represent the pulsating nature of blood flow and heart motion. This dynamic approach allows the computational mesh to deform and transform according to physiological conditions, improving diagnostic precision while using efficient algorithms to handle the temporal variations.
2Measurement precision
If time-varying geometries are incorporated to accurately represent heart motion and blood flow, then measurement precision improves, but computational complexity and processing time increase
Solution Approach 1:
The computational model exploits the periodic nature of the cardiac cycle by using boundary conditions that repeat across heartbeats. This allows the simulation to capture time-varying geometries efficiently by leveraging the repetitive pattern of cardiac motion, reducing computational time while maintaining geometric accuracy.
Solution Approach 2:
The model uses medical imaging data (such as MRI or CT scans) to create accurate digital copies of the subject's cardiovascular geometry. These copied geometries are then used in the computational simulation, allowing time-varying representations without requiring direct real-time measurement during the simulation process.
3Measurement precision
If Fluid Structure Interaction algorithms are used to simulate blood flow and tissue interaction, then measurement precision improves, but device complexity and computational resources increase
Solution Approach 1:
The computational model uses an intermediary approach where the blood flow and tissue structure are coupled through shared boundary conditions and interaction interfaces. This FSI framework allows the two physical systems to influence each other accurately while using modular algorithms that manage computational complexity through systematic coupling strategies.
4Reliability
If subject-specific models are created for each patient, then reliability and diagnostic precision improve, but productivity and ease of manufacture decrease
Solution Approach 1:
The system performs preliminary processing of medical imaging data to automatically generate subject-specific geometries and mesh structures before the actual simulation. This preliminary action includes automated segmentation, geometry reconstruction, and mesh generation, which streamlines the workflow and improves productivity while maintaining the reliability of personalized modeling.
Solution Approach 2:
The computational model incorporates automated algorithms that self-adjust and optimize parameters based on the input medical imaging data. This self-service capability reduces manual intervention requirements, allowing subject-specific models to be generated efficiently without extensive expert involvement, thereby improving productivity while maintaining reliability.
Data Source
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AI summary
The invention regards a subject-specific simulation model of at least one component in the cardiovascular system for simulating blood flow and/or structural features. This simulation model can be used as a tool for cardiovascular diagnostic and / or treatment planning. The invention also regards non-invasive medical imaging of the cardiovascular system making it possible to detect and grade pathology related to both anatomical and physiological abnormalities. The subject-specific simulation model of a component in the cardiovascular system, for instance a pumping heart, is reconstructed by combining computational fluid dynamics (CFD) and/or fluid structure interaction (FSI) algorithms with medical imaging, such as for example ultrasound, MRI or CT. Such models make it possible to describe the complex flow phenomenon and provide flow details on a level not possible by medical imaging alone. The subject-specific models provided by the invention, is a tool for clinical decision-making, an objective support for health care professionals in making decisions prior to surgery. As such, the simulation model provides new insight into the outcome of surgical intervention on cardiac blood flow.