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Rujuta Kulkarni1, Gregory Boverman, David Isaacson

  • 1Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

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Electrical Impedance Tomography (EIT) breast imaging shows lower admittivity due to skin layers. A new layered model improves breast cancer imaging accuracy by accounting for these distinct tissue properties.

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Electrical Engineering

Background:

  • Electrical Impedance Tomography (EIT) is utilized for breast cancer imaging, aiming to map internal admittivity distributions.
  • Observed admittivities in compressed breasts during EIT are lower than in previous whole-chest imaging studies.
  • This discrepancy is attributed to a thin, low-admittivity skin layer significantly influencing measurements in compressed breasts.

Purpose of the Study:

  • To develop and validate a layered analytical forward model for more accurate breast imaging in EIT.
  • To compare the forward solution of the layered model with the traditional homogeneous model.
  • To demonstrate the enhanced reconstruction accuracy of embedded targets using the layered model.

Main Methods:

  • Development of a three-layer analytical forward model: two outer layers representing skin (low admittivity) and a central layer for breast tissue (high admittivity).
  • Derivation of the forward solution for this specific layered geometry.
  • Comparative analysis of the layered model's forward solution against the homogeneous model's solution.
  • Demonstration of improved target reconstruction in a layered phantom by replacing the homogeneous forward solution with the layered one.

Main Results:

  • The layered model accurately represents the admittivity distribution, incorporating distinct skin and tissue layers.
  • The forward solution for the layered model differs significantly from the homogeneous model, particularly due to the skin layers.
  • Replacing the homogeneous forward solution with the layered solution led to a demonstrable improvement in reconstructing an embedded target within a layered body.

Conclusions:

  • The developed layered analytical forward model provides a more accurate representation of breast anatomy for EIT.
  • Accounting for the thin, low-admittivity skin layers is crucial for precise admittivity mapping in compressed breast EIT.
  • The layered model significantly enhances the accuracy of breast cancer imaging reconstructions compared to homogeneous models.