A semi-automated method for patient-specific computational flow modelling of left ventricles

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Science

Background:

• Patient-specific computational fluid dynamics (CFD) modeling of the left ventricle (LV) aids in visualizing ventricular flow patterns.
• Current methodologies are operator-dependent, limiting efficiency and consistency due to manual processes and inherent uncertainties.

Purpose of the Study:

• To develop a semi-automated method for patient-specific LV CFD modeling.
• To reduce operator dependency and enhance the efficiency and consistency of LV flow simulations.

Main Methods:

• Utilized magnetic resonance imaging (MRI) derived coarse geometry data of the LV endocardium.
• Refined LV geometry to eliminate rough edges.
• Reconstructed meshes for all time frames and performed numerical solutions for intra-ventricular flow.

Main Results:

• The semi-automated approach yielded comparable CFD results to clinically validated LV flow models.
• Demonstrated minimal operator involvement in the modeling process.
• Showcased the capability of the method using patient-specific volunteer data.

Conclusions:

• The proposed semi-automated method effectively mitigates operator dependency in patient-specific LV CFD modeling.
• This technique offers a more efficient and consistent approach to analyzing intra-ventricular flow dynamics.
• The findings support the clinical applicability of this streamlined CFD modeling technique.