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Graph theory applications in congenital heart disease
1Department of Medical Imaging, National Taiwan University Hospital and Children Hospital, National Taiwan University, 7 Chung-Shan South Road, Taipei, 10002, Taiwan.
Scientific Reports
|July 10, 2023
Summary
Graph theory offers a novel way to map complex congenital heart diseases (CHDs). This method represents blood flow pathways, aiding in the analysis and potential AI development for CHDs.
Area of Science:
- Cardiovascular Science
- Network Science
- Medical Imaging
Background:
- Congenital heart diseases (CHDs) are characterized by complex abnormalities in cardiac structures and blood flow.
- Traditional methods may not fully capture the intricate network dynamics of CHDs.
- Graph theory provides a powerful framework for analyzing complex networks.
Purpose of the Study:
- To introduce a novel graph theory-based method for representing congenital heart diseases (CHDs).
- To demonstrate the application of this method in visualizing and analyzing specific CHDs like tetralogy of Fallot (TOF) and transposition of the great arteries (TGA).
- To explore the potential of this approach in advancing CHD research and artificial intelligence applications.
Main Methods:
- Representing cardiac structures and blood flow as graphs, with vertices as blood spaces and edges as directed blood flow.
- Constructing directed graphs and binary adjacency matrices for normal and abnormal heart conditions.
- Developing weighted adjacency matrices using 4D flow MRI data, incorporating blood flow velocities for specific CHD cases.
Main Results:
- Successfully constructed directed graphs and binary adjacency matrices for various CHD models, including TOF and TGA.
- Generated weighted adjacency matrices for repaired TOF using 4D flow MRI data, quantifying blood flow dynamics.
- Visualized complex blood flow patterns in CHDs using graph representations.
Conclusions:
- The proposed graph theory method offers a promising approach for representing and analyzing congenital heart diseases.
- This methodology can serve as a foundation for developing AI tools for CHD diagnosis and treatment planning.
- The technique facilitates a deeper understanding of CHD pathophysiology and aids future research endeavors.
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