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Electrical Properties Tomography: A Methodological Review.

Reijer Leijsen1, Wyger Brink1, Cornelis van den Berg2

  • 1Department of Radiology, C.J. Gorter Center for High Field MRI, Leiden University Medical Center, 2333ZA Leiden, The Netherlands.

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Summary
This summary is machine-generated.

Electrical properties tomography (EPT) maps conductivity and permittivity using MRI. This review unifies EPT techniques and introduces machine learning algorithms for advanced imaging.

Keywords:
conductivityelectrical properties tomography (EPT)inversionmagnetic resonance imaging (MRI)permittivity

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Electrical Engineering

Background:

  • Electrical properties tomography (EPT) is a non-invasive imaging technique.
  • It utilizes magnetic resonance (MR) systems to map tissue conductivity and permittivity.
  • Understanding these electrical properties is crucial for various medical applications.

Purpose of the Study:

  • To provide a comprehensive overview of Electrical Properties Tomography (EPT) techniques.
  • To unify various analytical EPT methods under a single mathematical formulation.
  • To introduce machine learning-based algorithms for EPT.

Main Methods:

  • Detailed formulation of physical equations underlying analytical EPT techniques.
  • Harmonization of diverse EPT methods into a unified framework.
  • Description of machine learning algorithms applied to EPT data.

Main Results:

  • A unified mathematical framework for numerous analytical EPT techniques.
  • Clear definitions of data requirements and acquisition for EPT.
  • Introduction to machine learning approaches for enhanced EPT analysis.

Conclusions:

  • The review offers a harmonized perspective on EPT methodologies.
  • It clarifies data needs and mathematical underpinnings of EPT.
  • Machine learning integration promises advancements in EPT applications.