Electrical impedance tomography (EIT) is developed to investigate the internal conductivity changes of an object through a series of boundary electrodes, and has become increasingly attractive in a broad spectrum of applications. However, the design of optimal tomography image reconstruction algorithms has not achieved the adequate level of progress and matureness. In this paper, we propose an efficient and high-resolution EIT image reconstruction method in the framework of sparse Bayesian learning. Significant performance improvement is achieved by imposing structure-aware priors on the learning process to incorporate the prior knowledge that practical conductivity distribution maps exhibit clustered sparsity and intra-cluster continuity. The proposed method not only achieves high-resolution estimation and preserves the shape information even in low signal-to-noise ratio scenarios but also avoids the time-consuming parameter tuning process. The effectiveness of the proposed algorithm is validated through comparisons with state-of-the-art techniques using extensive numerical simulation and phantom experiment results.

A cost-effective LED/photodiode(PD)-based time-domain luminescent lifetime measuring device with rugged electronics and simplified algorithms was assembled and successfully used to characterize oxygen sensing films, by continuously monitoring phosphorescence lifetime changes of phosphorescent platinum octaethylporphyrin (PtOEP) in cardo poly(aryl ether ketone) polymer (IMPEK-C) vs. variation of the oxygen partial pressure in a gas mixture (O(2)/N(2)). The results determined by both phosphorescence lifetime and intensity monitoring were compared and the lifetime mode gave results which are in good agreement with the intensity mode. The lifetime-based linear Stern-Volmer plot indicates that the PtOEP molecules are nearly homogeneously distributed in the sensing film. The phosphorescent lifetime of the PtOEP film changes from 75 micros in neat N(2) to less than 2 micros in neat O(2). The sensing system (by combination of the PtOEP sensing film with the home-assembled lifetime device) gives a high lifetime-based O(2) sensing resolution, e.g. about 2 micros Torr(-1) for low O(2) concentration (below 3.5% O(2), V/V). This feasible lifetime device configuration is affordable to most sensor laboratories and the device may facilitate the study of O(2) sensing material with the continuous lifetime monitoring method.

Inverse problems are essential to imaging applications. In this paper, we propose a model-based deep learning network, named FISTA-Net, by combining the merits of interpretability and generality of the model-based Fast Iterative Shrinkage/Thresholding Algorithm (FISTA) and strong regularization and tuning-free advantages of the data-driven neural network. By unfolding the FISTA into a deep network, the architecture of FISTA-Net consists of multiple gradient descent, proximal mapping, and momentum modules in cascade. Different from FISTA, the gradient matrix in FISTA-Net can be updated during iteration and a proximal operator network is developed for nonlinear thresholding which can be learned through end-to-end training. Key parameters of FISTA-Net including the gradient step size, thresholding value and momentum scalar are tuning-free and learned from training data rather than hand-crafted. We further impose positive and monotonous constraints on these parameters to ensure they converge properly. The experimental results, evaluated both visually and quantitatively, show that the FISTA-Net can optimize parameters for different imaging tasks, i.e. Electromagnetic Tomography (EMT) and X-ray Computational Tomography (X-ray CT). It outperforms the state-of-the-art model-based and deep learning methods and exhibits good generalization ability over other competitive learning-based approaches under different noise levels.

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