Objective : Electrical impedance tomography (EIT) is a promising measurement technique in applications, especially in industrial monitoring and clinical diagnosis. However, two major drawbacks exist that limit the spatial resolution of reconstructed EIT images, i.e. the ‘soft field’ effect and the ill-posed problem. In recent years, apart from the development of reconstruction algorithms, some preprocessing methods for measured data or sensitivity maps have also been proposed to reduce these negative effects. It is necessary to find the optimal preprocessing method for various EIT reconstruction algorithms. Approach : In this paper, seven typical data preprocessing methods for EIT are reviewed. The image qualities obtained using these methods are evaluated and compared in simulations, and their applicable ranges and combination effects are summarized. Main results : The results show that all the reviewed methods can enhance the quality of EIT reconstructed images to different extents, and there is an optimal one under any given reconstruction algorithm. In addition, most of the reviewed methods do not work well when using the Tikhonov regularization algorithm. Significance : This paper introduces the preprocessing method to EIT, and the quality of reconstructed images obtained using these methods is evaluated through simulations. The results can provide a reference for practical applications.

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.

Electrical impedance tomography (EIT) is a radiation-free, fast, and convenient tomographic technology that uses only surface electrodes. EIT has the potential to provide continuous bedside monitoring of patients, for example, during mechanical ventilation. However, the soft-field nature of EIT leads to an ill-posed reconstruction problem that often results in multiple image solutions. This is exacerbated when EIT systems are used in variable measurement setups and scenarios, as few adjustment methods or EIT calibration approaches work reliably or require a reference phantom. The goals of this work are to design quality metrics to provide feedback for the EIT process to guide the system in adjusting the algorithm and parameters, for example, to alert the system in the event of poor image quality due to electrode connection variability. This then may be dealt with by physical electrode adjustment. In this article, the metrics, OAD and AL, representing object and artifact discreteness and artifact level, were designed to evaluate EIT image quality in an unsupervised way. Based on the quality index feedback, an image optimization process is proposed. The developed metrics and optimization methods were validated through data simulated in typical circular domain and thorax scenarios, and clinical data from human respiration. The conventional supervised evaluation metric, i.e., the correlation coefficient (COR) between the reference image and the reconstructed image, was used for comparison. The accuracy of new metrics was represented as the correlation coefficient of the new metric and COR. From the results, OAD and AL could improve the image quality by optimizing regularization parameters. The accuracy results of OAD and COR in the circular domain are 0.9429 and 0.9328, and those in thorax scenarios are 0.9741 and 0.8393. OAD and AL provide the evaluation of EIT images without using any prior knowledge or reference conductivity distribution, providing feedback to improve the reliability of EIT systems and reduce the risk of erroneous diagnosis.

Electrical impedance tomography (EIT) is a non-invasive imaging technology that is widely used in industrial and medical applications. The reconstruction methods are ill-posed and sensitive to the scale of perturbations and prior assumptions, so differential reconstruction is mostly used in practice. Absolute reconstruction is promising but has always been a challenge in practical applications. One of the obstacles is to determine the reference measurement frame from a uniform distributed domain. In this paper, we develop a reference measurement estimation technique for a homogeneous conductivity distribution with proportional variations. The concept of measurement-scale feature (MSF) is proposed to represent the scale of measurements, and five MSF operators are defined for EIT measurements. The proposed method estimates the prior reference measurements using the MSF of present measurements. The baseline reference frame is simulated with arbitrary conductivity with the same domain shape and electrode positions as the measured domain, and the MSF of the baseline frame. Simulations and experiments were carried out to verify the validity of the estimated reference measurements for absolute EIT (aEIT) reconstruction. Evaluations on reconstruction results and reference estimation errors show the effectiveness of this method for aEIT in various scenarios with a wide range of conductivity and different domain shapes. This study demonstrates an effective approach for determining the homogeneous reference frame, providing more potential for the application of aEIT in real-time pipeline detection and online clinical monitoring.

This paper introduces a sensitivity matrix decomposition regularization (SMDR) method for electric impedance tomography (EIT). Using k-means clustering, the EIT-reconstructed image can be divided into four clusters, derived based on image features, representing posterior information. The sensitivity matrix is then decomposed into distinct work areas based on these clusters. The elimination of smooth edge effects is achieved through differentiation of the images from the decomposed sensitivity matrix and further post-processing reliant on image features. The algorithm ensures low computational complexity and avoids introducing extra parameters. Numerical simulations and experimental data verification highlight the effectiveness of SMDR. The proposed SMDR algorithm demonstrates higher accuracy and robustness compared to the typical Tikhonov regularization and the iterative penalty term-based regularization method (with an improvement of up to 0.1156 in correlation coefficient). Moreover, SMDR achieves a harmonious balance between image fidelity and sparsity, effectively addressing practical application requirements.