Adaptive techniques in electrical impedance tomography reconstruction

Area of Science:

• Biomedical Engineering
• Computational Imaging
• Medical Physics

Background:

• Electrical Impedance Tomography (EIT) is a non-invasive imaging technique.
• Solving the inverse problem in EIT is computationally intensive.
• Balancing image accuracy and computational efficiency is crucial for EIT.

Purpose of the Study:

• To present an adaptive algorithm for solving the inverse problem in EIT.
• To improve the balance between image accuracy and computational efficiency.
• To enhance the performance of EIT image reconstruction.

Main Methods:

• Combined adaptive mesh refinement with the adaptive Kaczmarz method.
• Developed an iterative algorithm that adaptively generates current patterns and a locally-refined mesh.
• Utilized block Kaczmarz update steps for conductivity distribution estimation.

Main Results:

• Demonstrated the accuracy of the proposed algorithm through simulations and experimental results.
• Showcased the computational efficiency of the algorithm.
• Validated the algorithm's effectiveness in solving the EIT inverse problem.

Conclusions:

• The proposed adaptive algorithm effectively solves the EIT inverse problem.
• The combination of adaptive mesh refinement and Kaczmarz method enhances both accuracy and efficiency.
• This approach offers a promising solution for real-time EIT applications.