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.

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.

This paper presents the design and evaluation of a configurable, fast multi-frequency Electrical Impedance Tomography (mfEIT) system for real-time 2D and 3D imaging, particularly for biomedical imaging. The system integrates 32 electrode interfaces and the current frequency ranges from 10 kHz to 1 MHz. The system incorporates the following novel features. First, a fully adjustable multi-frequency current source with current monitoring function is designed. Second, a flexible switching scheme is developed for arbitrary sensing configuration and a semi-parallel data acquisition architecture is implemented for high-frame-rate data acquisition. Furthermore, multi-frequency digital quadrature demodulation is accomplished in a high-capacity Field Programmable Gate Array. At last, a 3D imaging software, visual tomography, is developed for real-time 2D and 3D image reconstruction, data analysis, and visualization. The mfEIT system is systematically tested and evaluated from the aspects of signal to noise ratio (SNR), frame rate, and 2D and 3D multi-frequency phantom imaging. The highest SNR is 82.82 dB on a 16-electrode sensor. The frame rate is up to 546 fps at serial mode and 1014 fps at semi-parallel mode. The evaluation results indicate that the presented mfEIT system is a powerful tool for real-time 2D and 3D imaging.

Image reconstruction of Electrical Impedance Tomography (EIT) is a highly nonlinear ill-posed inverse problem, which is sensitive to the measurement noise and model errors. An improved Convolutional Neural Network (CNN) method is proposed for the EIT lung imaging. The proposed method is optimized based on the Visual Geometry Group (VGG) model, adding the batch normalization (BN) layer, ELU activation function, global average pooling (GAP) layer, and radial basis function (RBF) neural network. These optimizations help speed up network convergence, and improve reconstruction accuracy and robustness. Nearly 10 thousand EIT simulation models generated from chest CT images of 60 patients are used for the network training. The chest deformation, lung hyperdilation and atelectasis are randomly simulated during the model generation process. The proposed method after training is tested through a series of simulation data and experimental models. The reconstruction quality is quantitatively compared by calculating the root mean square error (RMSE) and image correlation coefficient (ICC). On average, the proposed method achieves 0.082 RMSE and 0.892 ICC through experimental results. The proposed method achieves high-resolution and robust shape reconstructions with multiphase conductivity for EIT lung imaging, especially in the presence of the measurement noise and interference. The proposed method is promising in providing quantitative images for potential clinical applications, such as human thorax imaging.

Real-time quantitative imaging is becoming highly desirable to study nondestructively the biological behavior of 3-D cell culture systems. In this paper, we investigate the feasibility of quantitative imaging/monitoring of 3-D cell culture processes via electrical impedance tomography (EIT), which is capable of generating conductivity images in a non-destructive manner with high temporal resolution. To this end, a planar miniature EIT sensor amenable to standard cell culture format is designed, and a 3-D forward model for the sensor is developed for 3-D imaging. Furthermore, a novel 3-D-Laplacian and sparsity joint regularization algorithm is proposed for enhanced 3-D image reconstruction. Simulation phantoms with spheres at various vertical and horizontal positions were imaged for 3-D performance evaluation. In addition, experiments on human breast cancer cell spheroid and a triangular breast cancer cell pellet were carried out for experimental verification. The results have shown that the stable measurement on high conductive cell culture medium and the significant improvement of image quality based on the proposed regularization method are achieved. It demonstrates the feasibility of using the miniature EIT sensor and 3-D image reconstruction algorithm to visualize 3-D cell cultures, such as spheroids or artificial tissues and organs. The established work would expedite real-time quantitative imaging of 3-D cell culture for assessment of cellular dynamics.