EIT-MESHER

EIT-MESHER generates high-quality tetrahedral meshes from binary masks of 3D volume segmentations for Finite Element Model (FEM)–based simulations such as Electrical Impedance Tomography (EIT) and related tomographic reconstructions.


Key Features:

  • Implementation: Implemented in C++ and leverages the Computational Geometry Algorithms Library (CGAL) for mesh construction.
  • Input data: Accepts binary masks of 3D volume segmentations as the source for mesh generation.
  • Mesh output: Produces high-quality tetrahedral meshes suitable for Finite Element Models (FEM).
  • Non-linear refinement: Supports custom, non-linear refinement strategies to concentrate mesh resolution in critical regions such as around electrodes.
  • Robustness and efficiency: Uses CGAL-based algorithms to provide robust and efficient mesh generation and quality control.
  • Application-specific quality: Enhances mesh quality to improve accuracy of EIT reconstructions and other inverse-problem simulations.

Scientific Applications:

  • Electrical Impedance Tomography (EIT): Generates FEM meshes tailored for EIT forward and inverse problem simulations, including electrode region refinement.
  • Diffuse Optical Tomography (DOT): Produces precise tetrahedral meshes to support DOT optical transport and reconstruction modeling in soft tissues.
  • FEM-based biomedical simulations: Provides meshes applicable to other biomedical and tomographic FEM simulations requiring localized refinement.

Methodology:

Implemented in C++ and built on CGAL, EIT-MESHER generates tetrahedral meshes from binary 3D segmentation masks and applies custom non-linear refinement criteria, including localized refinement around electrodes.

Topics

Details

License:
BSD-3-Clause
Tool Type:
command-line tool
Programming Languages:
C++, MATLAB
Added:
1/18/2021
Last Updated:
3/5/2021

Operations

Publications

Dowrick T, Avery J, Faulkner M, Holder D, Aristovich K. EIT-MESHER – Segmented FEM Mesh Generation and Refinement. Unknown Journal. 2020. doi:10.20944/preprints202005.0351.v2.

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