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Eye Irritation Test EIT for Hazard Identification of Eye Irritating Chemicals using Reconstructed Human Cornea-like Epithelial RhCE Tissue Model
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Higher order total variation regularization for EIT reconstruction.

Bo Gong1,2, Benjamin Schullcke3,4, Sabine Krueger-Ziolek3,4

  • 1Institute of Technical Medicine, Furtwangen University, VS-Schwenningen, Germany. Bo.Gong@hs-furtwangen.de.

Medical & Biological Engineering & Computing
|January 9, 2018
PubMed
Summary
This summary is machine-generated.

Total generalized variation (TGV) regularization improves electrical impedance tomography (EIT) reconstructions by reducing unrealistic staircase effects seen with total variation (TV) regularization, leading to more accurate conductivity imaging.

Keywords:
Electrical impedance tomographyInverse problemTotal generalized variation

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Area of Science:

  • Medical Imaging
  • Computational Science
  • Electrical Engineering

Background:

  • Electrical impedance tomography (EIT) is an ill-posed inverse problem for imaging conductivity distributions.
  • Total variation (TV) regularization is commonly used to stabilize EIT reconstructions but causes staircase artifacts.
  • Existing methods for reducing TV artifacts include higher-order differential operators.

Purpose of the Study:

  • Adapt total generalized variation (TGV) regularization to the finite element model (FEM) framework for EIT.
  • Evaluate the effectiveness of TGV regularization in EIT reconstruction compared to TV regularization.
  • Improve the realism and accuracy of EIT conductivity images.

Main Methods:

  • Implemented TGV regularization within a finite element model (FEM) for EIT.
  • Performed EIT reconstructions using both simulated and clinical data.
  • Compared TGV-based reconstructions against TV-based reconstructions and ground truth.

Main Results:

  • TGV regularization produced more realistic conductivity images than TV regularization.
  • The study demonstrated reduced staircase artifacts with TGV.
  • TGV-based reconstructions showed improved fidelity to ground truth in simulation and clinical data.

Conclusions:

  • TGV regularization is a viable and effective method for enhancing EIT image quality.
  • Adapting TGV to the FEM framework offers a promising approach for clinical EIT applications.
  • TGV regularization mitigates artifacts, providing more diagnostically relevant EIT images.