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Regularization Solver Guided FISTA for Electrical Impedance Tomography
Qian Wang1, Xiaoyan Chen1, Di Wang1
1School of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300457, China.
Sensors (Basel, Switzerland)
|February 28, 2023
Summary
This study introduces the Regularization Solver Guided Fast Iterative Shrinkage Threshold Algorithm (RS-FISTA) for electrical impedance tomography (EIT) reconstruction. RS-FISTA improves image quality and achieves faster convergence compared to existing methods.
Area of Science:
- Medical Imaging
- Computational Electromagnetics
- Applied Mathematics
Background:
- Electrical impedance tomography (EIT) reconstructs conductivity distributions but faces challenges with nonlinear, ill-posed inverse problems.
- Existing iterative EIT methods suffer from subjective initializations, slow convergence, and blurred image details.
Purpose of the Study:
- To develop a novel, fast-convergent iterative method for EIT inverse problems.
- To enhance EIT reconstruction accuracy and spatial resolution.
Main Methods:
- Proposed the Regularization Solver Guided Fast Iterative Shrinkage Threshold Algorithm (RS-FISTA).
- Incorporated an adaptive regularization parameter adjustment for initial guess generation.
- Utilized L1-norm regularization and Nesterov acceleration for gradient optimization.
Main Results:
- RS-FISTA demonstrated superior performance over Landweber, CG, NOSER, Newton-Raphson, ISTA, and FISTA methods.
- Achieved high image quality metrics: SSIM of 0.7253, RMSE of 3.44, and PSNR of 37.55.
- Exhibited stable convergence within 30 iterations, indicating efficiency.
Conclusions:
- RS-FISTA offers improved visualization and rapid convergence for EIT reconstruction.
- The method is validated as superior for both simulated and experimental EIT data.
