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Personalized treatment for cardiovascular diseases

Personalized treatment, which tailors healthcare decisions to each individual, has the potential to improve treatment efficacy. By considering patient-specific information, such as medical images, in our lab, we aim to create more efficient and effective methods for treating cardiovascular diseases. Our numerical simulations are a crucial part of this effort, providing insights into the best approach for each individual patient.



Develop personalized treatments for cardiovascular diseases based on patient-specific numerical models


  • Computational fluid dynamics and finite element modeling

  • Signal and image processing

  • Machine learning

Amirtahà Taebi

Amirtahà Taebi


Current members involved:


Past members involved:

Brooke Scardino
Haleigh Davidson

Hover the mouse over the figure to see details.


Deep Learning

We use deep learning in different steps of our numerical simulations, from image segmentation to prediction of the results.

Medical Imaging and Clinical Measurements

Our goal is to develop patient-specific solutions. Therefore, for each patient, we use medical images and clinical measurements to create numerical models that accurately represent the patient's health status.

Computational Domain

We use image processing and machine learning techniques to extract the geometry of interest from the medical images for every patient.

Mesh Generation

The quality and refinement of the mesh greatly impact the accuracy and efficiency of CFD simulations, influencing the ability to capture complex flow patterns and resolve boundary layer effects.

Boundary Conditions

The play an important role in obtaining accurate results. We use patient-specific clinical measurements, e.g., using echocardiography or magnetic resonance imaging, to determine the boundary conditions.

Simulation Results

We look into various hemodynamics parameters such as velocity profiles, wall shear stresses, and acoustic signatures.


Selected Publications

Other Publications

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