In this work, we consider the reconstruction of three-dimensional (3D) conductivity distribution using electrical impedance tomography (EIT) technique. A high-resolution and efficient algorithm is developed to solve the EIT inverse problem. The presented algorithm is extended upon a recently proposed novel EIT reconstruction approach based on structure-aware sparse Bayesian learning (SA-SBL). The correlation between proximal layers in the 3D geometry are incorporated into the structure prior to improve the reconstruction accuracy. In addition, an efficient approach based on approximate message passing is developed to accelerate the large-scale 3D learning process. To validate the algorithm, numerical experiments using real recorded data are conducted. The visual and quantitative-metric comparisons show that the proposed method outperforms the existing methods in terms of reconstruction accuracy and computational complexity in all test cases. The SA-SBL based reconstruction approach can preserve the 3D structure of medical volume, reduce the systematic artifacts, and improve the computational efficiency.

Frequency-difference electrical impedance tomography (fdEIT) was originally developed to mitigate the systematic artifacts induced by modeling errors when a baseline dataset is unavailable. Instead of fine anatomical imaging, only coarse anomaly detection has been addressed in current fdEIT research mainly due to its low spatial resolution. On the other hand, there has been not enough study on fdEIT reconstruction algorithm as well. In this article, we propose an efficient and high-spatial-resolution algorithm for simultaneously reconstructing multiple fdEIT frames corresponding to inject currents with multiple frequencies. The electrical impedance tomography reconstruction problem is considered within a hierarchical Bayesian framework, where both intratask spatial clustering and intertask dependency are automatically learned and exploited in an unsupervised manner. The computation is accelerated by adopting a modified marginal likelihood maximization approach. Real-data experiments are conducted to verify the recovery performance of the proposed algorithm.

There is currently a need to culture cells in 3D to better mimic the behaviour of cells growing in the natural environment. In parallel, this calls for novel technologies to assess cell growth in 3D cell culture. In this study, we demonstrated both in silico and in vitro that cell viability inside large cell spheroids could be monitored in real time and label-free with electrical impedance tomography (EIT). Simulations using a single shell model and the effective media approximation (EMA) method were performed to prove the performance of EIT on spheroid imaging and viability monitoring. Then in vitro experiments were conducted to measure in real time a loss of cell viability in MCF-7 breast cancer spheroids when exposed to Triton X-100 and validate with conventional biochemical assays. It is shown that EIT has a spatial resolution of 1.14% and it could monitor the cell mortality over 20% of a spheroid under laboratory noise level. The reconstructed conductivity images for cell mortality induced by the chemical are clear and match the result in the cellular metabolic viability assay. Furthermore, the image reconstruction speed in the experiment was less than 0.3 seconds. Taken together, the results show the potential of EIT for non-destructive real-time and label-free cellular assays in the miniature sensor, providing physiological information in the applications of 3D drug screening and tissue engineering.

3-D Electrical Impedance Tomography (EIT) is an emerging technique for real-time and non-destructive 3-D cell culture imaging. This paper presents a pioneering study of scaffold-based 3-D cell culture imaging using a miniature planar EIT sensor. A 17-electrode miniature-planar EIT sensor equipped with a regular-shape 3-D printed scaffold was manufactured, modelled and characterized. In addition, an efficient 3-D image reconstruction method based on 3-D isotropic Total Variation and l1 joint regularization were proposed. Numerical simulation on scaffold phantoms and experimental study on time-varying distribution of MCF-7 cancer cell suspension within the scaffold were performed. Both simulation and experiment results suggest that using the miniature EIT sensor and the developed 3-D image reconstruction algorithms are able to achieve high quality, non-destructive scaffold-based 3-D cell culture imaging.

A novel image reconstruction algorithm for electrical impedance tomography using enhanced adaptive group sparsity with total variation constraint is proposed in this paper. The new algorithm simultaneously utilizes the prior knowledge of regional structure feature and global characteristic of the conductivity distribution. The regional structure feature is encoded by using an enhanced adaptive group sparsity constraint. Meanwhile, the global characteristic of inclusion boundary is considered by imposing total variation constraint on the whole image. An enhanced adaptive pixel grouping algorithm is proposed based on Otsu's thresholding method, which demonstrates good noise immunity. An accelerated alternating direction method of multipliers is utilized to solve the proposed problem for a faster convergence rate. The performance of the proposed algorithm is thoroughly evaluated by numerical simulation and experiments. Comparing with the state-of-the-art algorithms, such as the L1 regularization, total variation regularization, and our former work on adaptive group sparsity, the proposed method has demonstrated superior spatial resolution and better noise reduction performance. Combined with the total variation constraint, distinct boundary of inclusions has also been obtained.

In tissue engineering, cells are generally cultured in biomaterials to generate three-dimensional artificial tissues to repair or replace damaged parts and re-establish normal functions of the body. Characterizing cell growth and viability in these bioscaffolds is challenging, and is currently achieved by destructive end-point biological assays. In this study, we explore the potential to use electrical impedance tomography (EIT) as a label-free and non-destructive technology to assess cell growth and viability. The key challenge in the tissue engineering application is to detect the small change of conductivity associated with sparse cell distributions in regards to the size of the hosting scaffold, i.e., low volume fraction, until they assemble into a larger tissue-like structure. We show proof-of-principle data, measure cells within both a hydrogel and a microporous scaffold with an ad-hoc EIT equipment, and introduce the frequency difference technique to improve the reconstruction.