As an advanced measurement technique of non-radiant, non-intrusive, rapid response, and low cost, the electrical tomography (ET) technique has developed rapidly in recent decades. The ET imaging algorithm plays an important role in the ET imaging process. Linear back projection (LBP) is the most used ET algorithm due to its advantages of dynamic imaging process, real-time response, and easy realization. But the LBP algorithm is of low spatial resolution due to the natural ‘soft field’ effect and ‘ill-posed solution’ problems; thus its applicable ranges are greatly limited. In this paper, an original data decomposition method is proposed, and every ET measuring data are decomposed into two independent new data based on the positive and negative sensing areas of the measuring data. Consequently, the number of total measuring data is extended to twice as many as the number of the original data, thus effectively reducing the ‘ill-posed solution’. On the other hand, an index to measure the ‘soft field’ effect is proposed. The index shows that the decomposed data can distinguish between different contributions of various units (pixels) for any ET measuring data, and can efficiently reduce the ‘soft field’ effect of the ET imaging process. In light of the data decomposition method, a new linear back projection algorithm is proposed to improve the spatial resolution of the ET image. A series of simulations and experiments are applied to validate the proposed algorithm by the real-time performances and the progress of spatial resolutions.

Owing to its low cost, fast response, noninvasiveness, and non-radiation, electrical impedance tomography (EIT) has been applied to numerous fields. However, its spatial resolution is low due to the inherent ill-posed problem and the “soft field” effect. The L _p regularization (0 <; p <; 2) is effective for overcoming these disadvantages, and efforts have been made to use regularization from the most popular L ₂ to its variants L ₁ and L _1/2 . Nevertheless, L _p regularization is generally difficult to be solved fast and efficiently, and the selection of p yielding the best result is also a problem. In this paper, an adaptive re-weighted (ARW) algorithm with a general frame is presented to solve the L _p regularization for EIT, with p for each pixel determined adaptively in iterations. Experiments were carried out to validate the proposed algorithm. Results show that compared with other EIT algorithms, the ARW algorithm had a higher spatial resolution. Moreover, it can provide a wider range of selection for regularization parameter, which increases the practicality of the proposed algorithm.

Electrical impedance tomography (EIT) is an advanced visualizing technique with advantages such as real time, noninvasiveness, and low cost. However, the quality of EIT image still needs to be improved due to two inherent problems, i.e., ill-posed solution and ``soft-field'' effect. Nowadays, most of the existing EIT evaluation methods are supervised, relying on prior information or reference images, which are often unavailable in most applicable fields. In this paper, we proposed an unsupervised evaluation method based on a fast fuzzy C-means clustering algorithm. Compared with the most widely used evaluation methods, the new method could evaluate the reconstructed images without the need of any prior information or reference images. Simulations and experiments showed that the proposed method could efficiently evaluate the EIT images obtained using different algorithms with various parameters, and its capability of directly evaluating the reconstructed image made the evaluation process more useful in practical applications.

Biological tissue impedance spectroscopy can provide rich physiological and pathological information by measuring the variation of the complex impedance of biological tissues under various frequencies of driven current. Electrical Impedance Tomography (EIT) technique can measure the impedance spectroscopy of biological tissue in medical field. Before application, a key problem must be solved on how to generally distinguish normal tissues from the cancerous in terms of measurable EIT data. In this paper, the impedance spectroscopy characteristics of human lung tissue are studied. On the basis of the measured data of 109 lung cancer patients, Cole-Cole Circle radius (CCCR) and the complex modulus are extracted. In terms of the two characteristics, 71.6% and 66.4% samples of cancerous and normal tissues can be correctly classified, respectively. Furthermore, two characteristics of the measured EIT data of each patient consist of a two-dimensional vector and all such vectors comprise a set of vectors. When classifying the vector set, the rate of correctly partitioning normal and cancerous tissues can be raised to 78.2%. The main factors to affect the classification results on normal and cancerous tissues are generally analyzed. The proposed method will play an important role in further working out an efficient and feasible diagnostic method for potential lung cancer patients, and provide theoretical basis and reference data for electrical impedance tomography technology in monitoring pulmonary function.