Regularizing a linearized EIT reconstruction method using a sensitivity based factorization method
For practitioners of EIT, this work offers a non-iterative regularization that enhances shape reconstruction from limited electrode measurements.
The paper proposes a regularization of the linearized reconstruction method for EIT using a sensitivity-based factorization method, demonstrating improved anomaly localization and reduced ringing artifacts in numerical examples.
For electrical impedance tomography (EIT), most practical reconstruction methods are based on linearizing the underlying non-linear inverse problem. Recently, it has been shown that the linearized problem still contains the exact shape information. However, the stable reconstruction of shape information from measurements of finite accuracy on a limited number of electrodes remains a challenge. In this work we propose to regularize the standard linearized reconstruction method (LM) for EIT using a non-iterative shape reconstruction method (the factorization method). Our main tool is a discrete sensitivity-based variant of the factorization method (herein called S-FM) which allows us to formulate and combine both methods in terms of the sensitivity matrix. We give a heuristic motivation for this new method and show numerical examples that indicate its good performance in the localization of anomalies and the alleviation of ringing artifacts.