Patient-specific computational fluid dynamics: structured mesh generation from coronary angiography.

Area of Science:

• Biomedical Engineering
• Computational Fluid Dynamics
• Medical Imaging

Background:

• Patient-specific simulations are crucial for understanding hemodynamics in realistic vascular geometries.
• Current pre-processing and mesh generation are often time-consuming, operator-dependent, and yield suboptimal mesh quality.

Purpose of the Study:

• To present a semi-automatic methodology for patient-specific reconstruction and structured meshing of a left coronary tree from biplane angiography.
• To compare the performance of hexahedral meshes against unstructured tetrahedral meshes for hemodynamic simulations.

Main Methods:

• Developed a semi-automatic method for reconstructing patient-specific left coronary trees from biplane angiography.
• Generated seven hexahedral grids and compared them with nine unstructured tetrahedral grids with prismatic boundary layers.
• Performed steady-state blood flow simulations using Computational Fluid Dynamics (CFD) to calculate Wall Shear Stress (WSS).

Main Results:

• Hexahedral meshes demonstrated superior convergence compared to tetrahedral/prismatic meshes.
• For equivalent accuracy, hexahedral meshes required six times fewer cells and 14 times less computational time.
• Analysis of 99 percentile, area-weighted, and local WSS values confirmed the efficiency of hexahedral meshes.

Conclusions:

• Hexahedral meshes are superior to tetrahedral/prismatic meshes for calculating Wall Shear Stress (WSS) in patient-specific coronary artery simulations.
• The proposed semi-automatic methodology offers a more efficient approach to mesh generation.
• Hexahedral meshes should be preferred for accurate and computationally efficient hemodynamic analyses.