Methods of graph network reconstruction in personalized medicine

Area of Science:

• Biomedical Engineering
• Computational Fluid Dynamics
• Medical Imaging Analysis

Background:

• Accurate computational models are crucial for simulating physiological processes.
• Patient-specific vascular networks require detailed geometric data for modeling.
• Existing methods may lack efficiency in generating complex computational domains.

Purpose of the Study:

• To develop and validate algorithms for generating individualized computational domains from medical imaging datasets.
• To enable the creation of one-dimensional (1D) and coupled three-dimensional (3D)-1D hemodynamic models.
• To improve the automated segmentation and reconstruction of vascular networks.

Main Methods:

• Proposed algorithms for automated segmentation of vascular structures and centerline generation.
• Methods for 1D network reconstruction, correction, and local adaptation.
• Consideration of two centerline representations: skeletal segments and curved paths with radii.

Main Results:

• Demonstrated efficiency of proposed algorithms in reconstructing 1D vascular networks.
• Successful generation of individualized computational domains for hemodynamic modeling.
• Validation of methods using several 1D network reconstruction examples.

Conclusions:

• The developed methods facilitate the creation of patient-specific computational domains for hemodynamic and physiological modeling.
• The algorithms support both skeletal and curved centerline representations for network reconstruction.
• The approach enhances the capability for accurate blood flow and physiological process simulations in tubular structures.