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Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using Meshtool.

Aurel Neic1,2, Matthias A F Gsell1, Elias Karabelas1,3

  • 1Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria.

Softwarex
|July 2, 2020
PubMed
Summary
This summary is machine-generated.

Meshtool software automates complex cardiac mesh manipulation, reducing costs and improving efficiency for personalized in silico heart models. This enables faster creation of virtual heart cohorts for advanced cardiac research.

Keywords:
Geometric smoothingMesh generationMesh manipulation

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Area of Science:

  • Computational biology
  • Medical imaging analysis
  • Cardiovascular research

Background:

  • Personalized cardiac modeling requires complex mesh generation from imaging data.
  • Current workflows are time-consuming and expensive due to manual mesh manipulation.
  • Generating virtual cohorts necessitates efficient methods to capture cardiac variability.

Purpose of the Study:

  • To introduce Meshtool, a software for automating cardiac mesh manipulation.
  • To demonstrate Meshtool's capability in streamlining the creation of in silico cardiac models.
  • To highlight the efficiency and cost-reduction benefits of Meshtool in cardiac modeling.

Main Methods:

  • Meshtool implements algorithms for label field and geometric feature operations.
  • The software automates complex mesh manipulation tasks in cardiac modeling workflows.
  • Meshtool is designed for high performance and integration into existing pipelines.

Main Results:

  • Meshtool significantly increases the efficiency of generating personalized in silico cardiac models.
  • The software reduces the costs associated with complex mesh manipulation.
  • Automated workflows using Meshtool facilitate the creation of diverse virtual heart cohorts.

Conclusions:

  • Meshtool provides a powerful, automatable toolbox for advanced cardiac modeling.
  • By reducing costs and increasing efficiency, Meshtool supports broader adoption of in silico cardiac research.
  • The software enhances the ability to build virtual cohorts for studying cardiac variability.