An image-based computational hemodynamics study of the Systolic Anterior Motion of the mitral valve

Area of Science:

• Cardiovascular Imaging and Hemodynamics
• Computational Fluid Dynamics
• Medical Image Analysis

Background:

• Systolic Anterior Motion (SAM) of the mitral valve, often linked to Hypertrophic Obstructive Cardiomyopathy (HOCM), causes functional subaortic stenosis.
• Current diagnostic methods may underestimate SAM severity, increasing heart failure risk.
• Accurate SAM assessment is crucial for effective patient management and treatment planning.

Purpose of the Study:

• To introduce a novel computational pipeline for quantitative assessment of SAM using cardiac cine-MRI data.
• To improve the evaluation of SAM severity and its impact on left ventricular outflow tract obstruction.
• To provide insights for surgical interventions like septal myectomy.

Main Methods:

• Development of a computational pipeline integrating cardiac cine-MRI image processing and Computational Fluid Dynamics (CFD).
• Utilizing an Arbitrary Lagrangian-Eulerian approach for patient-specific left ventricular geometry and motion.
• Employing a resistive method to immerse the mitral valve within the CFD computational domain.

Main Results:

• Parametric study assessing clinically relevant flow and pressure indicators for varying SAM severities.
• Quantitative evaluation of pathological conditions related to SAM.
• Demonstration of the pipeline's capability to provide insights into hemodynamic alterations.

Conclusions:

• The proposed computational pipeline offers a more accurate and quantitative assessment of SAM compared to traditional methods.
• This approach can aid in better understanding the severity of SAM and its hemodynamic consequences.
• Findings support improved decision-making for surgical treatments, such as septal myectomy.