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Comparative study of left atrium epicardial fat tissue pattern using persistent homology approach
Deepa Deepa1, Yashbir Singh2,3, Wathiq Mansoor4
1Biomedical Engineering, Chung Yuan Christian University, Zhongli, Taiwan.
BMC Research Notes
|September 15, 2022
Summary
Persistent homology analysis successfully distinguished between left atrium epicardial fat and non-fat tissue. This topological data analysis approach aids in understanding atrial fibrillation (A-fib) causes.
Area of Science:
- Cardiology
- Medical Imaging
- Topology
Background:
- Atrial Fibrillation (A-fib) is linked to increased epicardial fat.
- Topological features can analyze complex medical data patterns.
Purpose of the Study:
- Evaluate topological patterns of left atrium epicardial fat tissue.
- Differentiate between fat and non-fat tissue using persistent homology.
Main Methods:
- Applied topological data analysis to CT images of left atrium tissue patches.
- Utilized persistent homology (PH) to extract distinguishing features.
- Categorized patches into epicardial fat and non-fat tissue groups.
Main Results:
- Successfully discriminated between left atrium epicardial fat and non-fat tissue.
- Epicardial fat tissue showed a smaller Betti number range (0-30) compared to non-fat tissue (0-100).
- Non-fat tissue exhibited more complex topology indicated by a wider Betti number range.
Conclusions:
- Persistent homology is effective for analyzing cardiac tissue composition.
- Topological patterns can serve as biomarkers for conditions like A-fib.
- This method offers a novel approach to understanding epicardial fat distribution.
