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Updated: Jan 15, 2026

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
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Stochastic parameter prediction in cardiovascular problems.

Kabir Bakhshaei1, Sajad Salavatidezfouli1,2, Giovanni Stabile3

  • 1Mathematics Area, mathLab, SISSA, Trieste, Italy.

Computer Methods in Biomechanics and Biomedical Engineering
|October 9, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to improve cardiovascular flow modeling using computational fluid dynamics (CFD) and an Ensemble Kalman filter. The approach enhances real-time boundary data accuracy for better disease prediction.

Keywords:
Ensemble Kalman FilterStochastic data assimilationbayesian inversioncardiovascular flowscomputational hemodynamicsuncertainty quantification

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Area of Science:

  • Biomedical Engineering
  • Computational Science
  • Cardiovascular Research

Background:

  • Accurate velocity boundary data is crucial for high-fidelity cardiovascular flow modeling.
  • In-vivo data, such as 4D flow MRI, often suffers from noise and low resolution, impacting wall shear stress (WSS) estimations.
  • WSS is a key factor in predicting cardiovascular diseases like atherosclerosis.

Purpose of the Study:

  • To develop a real-time method for refining velocity boundary estimates in cardiovascular flow models.
  • To improve the accuracy of patient-specific wall shear stress predictions.
  • To enhance the reliability of cardiovascular diagnostics and treatments.

Main Methods:

  • A stochastic data assimilation approach was developed, integrating Computational Fluid Dynamics (CFD) with an Ensemble Kalman filter.
  • The method was tested on both two-dimensional (2D) and three-dimensional (3D) vascular models.
  • Real-time refinement of boundary data was achieved through the proposed assimilation technique.

Main Results:

  • The proposed method significantly reduced errors in boundary estimates.
  • Error reduction was below 3% in 2D vascular models.
  • Error reduction was approximately 7% in 3D vascular models.

Conclusions:

  • The developed stochastic data assimilation method effectively enhances the accuracy of cardiovascular flow boundary data.
  • Improved boundary accuracy leads to more reliable patient-specific wall shear stress predictions.
  • This advancement supports more accurate cardiovascular diagnostics and personalized treatment strategies.