Regularized Shallow Image Prior for Electrical Impedance Tomography

Untrained neural network prior (UNNP)-based algorithms have gained increasing popularity in biomedical imaging, offering superior performance compared to handcrafted priors and requiring no training. UNNP-based methods typically rely on deep architectures, known for their excellent feature extraction ability compared to shallow ones. Contrary to common UNNP-based approaches, we propose a regularized shallow image prior (R-SIP) method that employs a three-layer multilayer perceptron (MLP) as the UNNP in regularizing 2-D and 3-D electrical impedance tomography (EIT) inversion and utilizes the handcrafted regularization to promote and stabilize the inversion process. The proposed algorithm is comprehensively evaluated on both simulated and real-world geometric and lung phantoms. We demonstrate significantly improved EIT image quality compared to conventional regularization-based algorithms, particularly in terms of structure preservation-a longstanding challenge in EIT. We reveal that three-layer MLPs with various architectures can achieve similar reconstruction quality, indicating that the proposed R-SIP-based algorithm involves fewer architectural dependencies and entails less complexity in the neural network.