Reconstruction of electrical impedance tomography (EIT) images based on the expectation maximum (EM) method

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Computational Science

Background:

• Electrical impedance tomography (EIT) reconstructs internal conductivity distributions from electrical measurements.
• EIT image reconstruction is a non-linear, ill-posed inverse problem.
• Traditional methods like Tikhonov and conjugate gradient (CG) can introduce artifacts due to negative values in solutions, especially with noisy data.

Purpose of the Study:

• To introduce and evaluate the expectation maximization (EM) method for solving the EIT inverse problem.
• To address the issue of negative values and artifacts in EIT reconstructed images.
• To compare the performance of the EM method against traditional regularization techniques.

Main Methods:

• The EIT mathematical model was transformed into a non-negatively constrained likelihood minimization problem.
• The gradient projection-reduced Newton (GPRN) iteration method was employed to find the solution.
• Parameter selection strategies for the EM method were discussed.

Main Results:

• The expectation maximization (EM) method demonstrated superior performance in EIT image reconstruction.
• EM method yielded higher quality reconstructed images compared to Tikhonov and CG methods.
• The EM method effectively reduced artifacts, even in the presence of noise and without explicit non-negative processing.

Conclusions:

• The expectation maximization (EM) method is a robust statistical approach for EIT image reconstruction.
• EM method offers significant advantages over traditional regularization techniques for EIT.
• This method provides a valuable tool for obtaining more accurate and artifact-free EIT images.