An integrated geometric modelling framework for patient-specific computational haemodynamic study on wide-ranged...

Area of Science:

• Biomedical Engineering
• Computational Fluid Dynamics
• Medical Imaging Analysis

Background:

• Patient-specific haemodynamic computations are crucial for understanding cardiovascular diseases like atherosclerosis and aneurysms.
• Challenges exist in accurately modeling vascular geometry from medical images due to noise and limited resolution.
• Accurate 3D modeling of arterial networks is essential for patient-specific computational haemodynamic studies.

Purpose of the Study:

• To develop an integrated framework for patient-specific computational haemodynamic studies.
• To achieve accurate 3D vascular geometry reconstruction and geometric feature quantification of arterial networks.
• To improve the understanding of the relationship between vascular morphology and haemodynamic forces in cardiovascular disease.

Main Methods:

• Developed an integrated framework for arterial network modeling.
• Utilized 3D vascular geometry reconstruction and quantification of geometric features.
• Employed centerline recognition to identify arterial network connectivity for improved modeling.

Main Results:

• The proposed method demonstrated accuracy and effectiveness in modeling an intracranial arterial network.
• The framework reconstructs a wider range of vascular networks with greater anatomical accuracy compared to the marching-cubes (MC) method.
• The method shows particular improvement in peripheral circulation where image resolution is low.

Conclusions:

• The developed framework provides a more anatomically accurate method for patient-specific vascular geometry modeling.
• This approach enhances the reliability of computational haemodynamic studies, especially in challenging low-resolution imaging scenarios.
• The integrated framework aids in understanding the 'geometric risk factor' in cardiovascular diseases by linking morphology and haemodynamics.