Electrical Impedance Tomography reconstruction using l1 norms for data and image terms

Area of Science:

• Medical Imaging
• Computational Electromagnetics
• Biomedical Engineering

Background:

• Electrical Impedance Tomography (EIT) reconstructs internal conductivity from surface measurements.
• EIT faces challenges with low spatial resolution and sensitivity to measurement noise.
• Traditional EIT reconstruction often uses l(2) norms.

Purpose of the Study:

• To introduce and evaluate a general iterative method for EIT image reconstruction.
• To explore the application of l(1) and l(2) norms in EIT reconstruction.
• To address the limitations of low spatial resolution and measurement error sensitivity in EIT.

Main Methods:

• A general lagged diffusivity type iterative method was developed for EIT.
• The method allows flexible selection of l(1) and/or l(2) minimizations for data residue and image prior terms.
• The proposed method was tested for EIT image reconstruction.

Main Results:

• The proposed iterative method demonstrated flexibility in choosing minimization norms.
• Utilizing l(1) norms for the data residue term reduced sensitivity to measurement errors.
• Employing l(1) norms for the image prior term effectively reduced edge blurring, improving spatial resolution.

Conclusions:

• The developed iterative algorithm offers a flexible approach to EIT reconstruction.
• l(1) norm-based reconstruction provides significant advantages over traditional l(2) methods for EIT.
• The findings highlight the potential of l(1) regularization for enhancing EIT image quality.