Comparison between a novel compressed sensing-based neural network and traditional neural network approaches for electrical impedance tomography reconstruction

Electrical Impedance Tomography (EIT) is a non-destructive and non-radioactive imaging technique used to detect anomalies in a material of interest. Applications of EIT range from medical imaging and early tumor detection to identifying structural damage. Within the past decade, deep learning (DL)-based EIT reconstruction has been an emerging field of study as it shows promise in addressing many of the challenges associated with the non-linear, ill-conditioned nature of EIT inverse problems. The DL-based approach allows for the conductivity of materials to be reconstructed directly through Neural Networks (NNs) as opposed to iteratively with conventional inverse reconstruction algorithms. So far, the reported DL-based NNs for EIT have mostly been trained by minimizing the Mean Squared Error (MSE) between the predicted and "true" outputs (i.e., conductivity distributions). The performance of these current NNs heavily relies on both the quality and quantity of training data. The NNs trained with simulated data may perform poorly with experimental data. On the other hand, generating sufficient experimental data NN training can be extremely expensive and time-consuming, if feasible at all. To advance the DL-based reconstruction for EIT, this study develops a novel NN architecture, trained with a custom loss function, that serves as a surrogate model for the compressed sensing-based EIT reconstruction algorithm. In other words, the NN is trained to mimic a compressed sensing algorithm that performs the EIT conductivity reconstruction. This approach enables the NN to accurately capture the electrical properties and characteristics of the sensing domain when trained with limited data of varying quality. The performance of the proposed NN was compared to other DL models trained with the traditional MSE loss function by evaluating their reconstruction resolution, accuracy, and other training metrics.