An analytical layered forward model for breasts in electrical impedance tomography

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Electrical Engineering

Background:

• Electrical impedance tomography (EIT) shows promise for breast cancer detection due to differing electrical properties of normal and malignant tissues.
• Breast imaging is challenging due to inhomogeneous structures, specifically thin, low-admittivity skin layers over high-admittivity tissue.
• Skin's electrical properties exhibit significant frequency-dependent variations, complicating accurate modeling.

Purpose of the Study:

• To propose and validate a layered forward model for EIT that incorporates skin layers.
• To develop and assess an iterative method for estimating skin and breast tissue admittivities.
• To demonstrate the clinical utility of the layered model in improving breast cancer detection.

Main Methods:

• Developed a three-layer (skin-breast-skin) forward model accounting for admittivity differences and frequency variations.
• Implemented an iterative algorithm to estimate admittivities from simulated and experimental EIT data.
• Compared the performance of the layered model against a traditional homogeneous model using simulated, experimental, and clinical data.

Main Results:

• The layered forward model accurately represents breast geometry and electrical properties compared to homogeneous models.
• The iterative method demonstrates robustness and accuracy in estimating admittivities from measured data.
• Reconstruction of embedded targets and analysis of clinical data show significant improvements with the layered model.

Conclusions:

• The proposed layered forward model enhances EIT accuracy for breast imaging by incorporating skin properties.
• This improved modeling approach offers better diagnostic potential for breast cancer detection using EIT.
• The study highlights the importance of accounting for anatomical and electrical heterogeneities in EIT.