Robust automated calcification meshing for personalized cardiovascular biomechanics

Area of Science:

• Biomedical Engineering
• Computational Biology
• Cardiovascular Research

Background:

• Cardiovascular calcification significantly impacts disease progression and treatment outcomes.
• Manual reconstruction of calcification for simulations is time-consuming and limits widespread use.
• Existing meshing techniques struggle to incorporate patient-specific calcification.

Purpose of the Study:

• To develop an automated image-to-mesh algorithm for patient-specific cardiovascular calcification.
• To overcome the bottleneck of manual meshing in computational simulations.
• To enable robust incorporation of calcification onto cardiovascular tissue meshes.

Main Methods:

• An end-to-end automated image-to-mesh algorithm was developed.
• The algorithm integrates patient-specific calcification data into cardiovascular tissue meshes.
• Validation was performed using extensive simulations of aortic stenosis and Transcatheter Aortic Valve Replacement.

Main Results:

• The automated algorithm reduced meshing time from hours to approximately one minute.
• The method successfully incorporated patient-specific calcification into meshes.
• Simulations demonstrated accurate modeling of aortic stenosis and Transcatheter Aortic Valve Replacement.

Conclusions:

• The proposed algorithm significantly accelerates the process of creating patient-specific cardiovascular models.
• This tool facilitates the development and application of personalized cardiovascular biomechanics.
• The method addresses a critical limitation in current computational simulation workflows.