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Related Experiment Videos

CFD validation using in-vitro MRI velocity data - Methods for data matching and CFD error quantification.

Carolin Wüstenhagen1, Kristine John1, Sönke Langner2

  • 1Institute of Fluid Mechanics, University of Rostock, Justus-von-Liebig-Weg 2, 18059, Rostock, Germany.

Computers in Biology and Medicine
|February 5, 2021
PubMed
Summary

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This study presents a routine to validate Computational Fluid Dynamics (CFD) simulations using in-vitro Magnetic Resonance Imaging (MRI) velocity data. This framework helps improve the accuracy of blood flow predictions in patient-specific models for better medical diagnoses.

Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Computational Fluid Dynamics

Background:

  • Computational Fluid Dynamics (CFD) simulations are valuable for predicting blood flow in patient-specific geometries, aiding diagnosis and treatment planning.
  • However, inaccuracies in CFD solutions necessitate understanding simulation errors and calibrating numerical models.
  • In-vitro velocity-encoded Magnetic Resonance Imaging (MRI) offers a versatile method for validating these CFD models.

Purpose of the Study:

  • To compare Computational Fluid Dynamics (CFD) predictions with in-vitro MRI velocity data.
  • To analyze and estimate simulation errors in medical CFD models.
  • To present a routine for validating patient-specific CFD data using experimental MRI measurements.

Main Methods:

Keywords:
Computational fluid mechanicsMagnetic resonance velocimetryReynolds similaritySimulation errorThree-dimensional geometry matching

Related Experiment Videos

  • Fabrication of a scaled, patient-specific model for high-resolution MRI.
  • Alignment of measured flow geometry with CFD data using Coherent Point Drift and Iterative Closest Point algorithms.
  • Interpolation of aligned datasets onto a common grid for point-to-point comparison and deviation calculation to estimate simulation error.

    • Main Results:

    • A three-step routine was developed and successfully tested on a patient-specific cerebral aneurysm model.
    • The routine enables reliable estimation of global and local deviations between CFD and MRI velocity data.
    • The study demonstrated a framework for validating medical CFD using in-vitro MRI velocity data.

    Conclusions:

    • The presented routine provides a robust framework for validating medical Computational Fluid Dynamics (CFD) using in-vitro MRI velocity data.
    • Accurate validation of CFD simulations is crucial for reliable blood flow prediction in clinical applications.
    • This approach enhances the trustworthiness of CFD in medical diagnosis and treatment planning.