Morphological Variation in the Human Mitral Valve Using Statistical Shape Modelling

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Anatomy

Background:

• Statistical Shape Modelling (SSM) provides a framework for analyzing anatomical variability.
• Virtual patient cohorts are crucial for in silico clinical trials and medical device development.

Purpose of the Study:

• To develop a Statistical Shape Model (SSM) of the mitral valve.
• To assess the physiological relevance and geometric diversity captured by the model.
• To demonstrate the utility of SSMs for generating virtual patient populations.

Main Methods:

• Utilized 72 contrast-enhanced computed tomography angiography (CTA) scans of the heart.
• Applied Principal Component Analysis (PCA) to identify dominant modes of shape variation.
• Developed an SSM of the mitral valve anatomy.

Main Results:

• The SSM successfully captured dominant modes of shape variation consistent with known anatomical patterns.
• The model effectively represents the geometric diversity of the mitral valve.
• Validated the physiological relevance of the developed SSM.

Conclusions:

• SSMs are effective for generating virtual patient populations from medical imaging data.
• The developed mitral valve SSM supports applications in preclinical testing, device design, and personalized medicine.
• This approach facilitates the integration of SSMs into computational modeling pipelines.