Electrical tomography (ET) has emerged as a safe and real-time imaging technology for decades due to its fast, non-invasive, and radiation-free characteristics. By reconstructing the spatial distribution or dynamic changes in the electrical properties of target regions, ET finds broad applications in non-destructive testing, industrial process monitoring, and biomedical research. Recent advancements in deep learning have significantly enhanced the accuracy, efficiency, and robustness of ET image reconstruction, particularly in electrical impedance tomography (EIT), electrical capacitance tomography (ECT), and electromagnetic tomography (EMT). Despite the growing interest, challenges persist in handling the ill-posed nature of ET imaging, especially for EMT, which remains underexplored compared to EIT and ECT. This review delves into the fundamental principles of ET and surveys recent progress in deep learning-driven approaches. We focus on two key paradigms: direct reconstruction networks (data-driven) and hybrid frameworks that integrate deep learning with classical algorithms (image-driven). The discussion highlights emerging trends, including multimodal ET integration, dataset optimization, and the design of advanced neural architectures tailored for ET-specific constraints. This study underscores the potential of deep learning to redefine ET systems, paving the way for more intelligent, versatile, and accurate imaging solutions.

A non-contact Laser-EMAT integrated system (LEIS) that uses a pulse laser as a generator and an Electromagnetic acoustic transducer (EMAT) as a detector is proposed. To explain the LEIS could simultaneously measure the width and the depth of metal plate, the finite element model (FEM) of the LEIS, which can describe the entire process from laser generates ultrasound to EMAT receives ultrasound, is established. In detail, the intensity ratios of Rayleigh wave, Shear wave and Longitudinal wave excited by laser under the consideration of three components, the particle displacement, the particle vibration velocity in out-plane direction and particle vibration velocity in in-plane direction, are focused. Combining the characteristics of the ratio and the EMAT as a velocity sensor, the Rayleigh wave and Longitudinal wave in the case of the particle vibration velocity in out-plane direction are chosen as the using waveforms, and the changes of directivity of various waveforms with laser parameters are studied. Furthermore, after the coil structure parameters and magnetic field direction of EMAT were reanalyzed and reconsidered, it is decided to use the coil with the wire spacing of 0.1mm and a number of turns of 8 to combine with the transverse magnetic field to simultaneously detect the Rayleigh wave and Longitudinal wave generated by the laser. Finally, the signal-to-noise ratio of the two waveforms reach a balanced value, and can be clearly distinguished and extracted to achieve the purpose of measuring the depth and width of the metal plate at the same time.

This paper presents electromagnetic inspection of in-service train wheel online for high-speed railway using a fast demodulation algorithm. The electromagnetic simulation demonstrates the defect can be identified by the amplitude variation of differential voltage induced in two pick-up coils. To demodulate the induced voltage, the Kalman-model-based demodulation algorithm is proposed, which can fast demodulate resultant signal of any arbitrary frequency and have higher anti-noise ability at high speed. Furthermore, the experimental platform for online electromagnetic inspection of in-service train wheel is designed. Both defect identification and speed estimation experiments are carried out. Finally, the experimental results demonstrate that the design of online electromagnetic inspection of in-service train wheel is effective and two defects can be identified by the differential induction voltage at speed over 300km/h. Furthermore, the Kalman-based demodulation have better performance in noise suppression at higher speed compared with other traditional demodulations.

In this article, the contributions are to further study and verify a bulk-wave electromagnetic acoustic transducer (EMAT) for the transverse crack detection of rail base and to propose an auxiliary method for achieving efficient detection. There is no verification for rail treated as a plate in the previous publication. For the verification, the wedge-like rail base is reasonably simplified into the front view and the side view to replace the 3-D finite-element model (FEM) simulation. In addition, the experiment to detect an artificial transverse crack of the standard rail specimen is carried out to verify the treatment of the wedge-like rail base. Both simulation and experimental results demonstrate that the wedge-like rail base can be inspected as a steel plate using a spiral-coil EMAT, but the only time of flight is hard to detect the transverse crack of the wedge-like rail base due to low SNR. For solving the problem of low SNR, this article proposes an auxiliary method of watching for the disturbances of the back-wall echo. Also, the limit condition of the auxiliary method is analyzed for practical applications.