Magnetic induction tomography (MIT) is a tomographic technique capable of imaging the passive electromagnetic properties of an object. It has the advantages of being contact-less and non-invasive, as the process involves interrogating the electromagnetic field of the imaging subject. As such, the potential applications of MIT are broad, with various domains of operation including biomedicine, industrial process tomography and non-destructive evaluation. Consequently, there is a rich—yet underexplored—research landscape for the practical applications of MIT. The aim of this review is to provide a non-exhaustive overview of this landscape. The fundamental principles of MIT are discussed, alongside the instrumentation and techniques necessary to obtain and interpret MIT measurements.

Electrical impedance tomography (EIT) is considered as a potential candidate for brain stroke imaging due to its compactness and potential use in bedside and emergency settings. The electrode-skin contact impedance and low conductivity of skull pose some practical challenges to EIT head imaging. This work studies the application of capacitively coupled electrical impedance tomography (CCEIT) in brain imaging for the first time. CCEIT is a new contactless EIT technique that uses voltage excitation without direct contact with the skin, as oppose to directly injecting current to the skin in EIT. Because the safety issue of a new technique should be strictly treated, simulation work based on a simplified head model was carried out to investigate the safety aspects of CCEIT. By comparing with standard EIT excited by a typical safe current level used in brain imaging, the safe excitation reference of CCEIT is obtained. This is done by comparing the maximum level of internal electrical field (internal current density) of EIT and that of CCEIT. Simulation results provide useful knowledge of excitation signal level of CCEIT and also show a critical comparison with traditional EIT. Practical experiments were carried out with a twelve-electrode CCEIT phantom, saline and carrot samples. Experimental results show the feasibility and potential of CCEIT for stroke imaging. In this work, the anomaly diameter resolution is 10 mm (1/18 of the phantom diameter), which indicates that small-volume stroke could be detected. This is achieved by a low excitation voltage of 1V, showing the possibility of even better performance when higher but yet safe level of excitation voltages is used.

Magnetic induction tomography (MIT) is an imaging modality with a wide range of potential applications due to its non-contact nature. MIT is a member of the electrical tomography family that faces the most difficult imaging challenges, due to its demanding measurement accuracy requirements and its difficult forward and inverse problems. This paper presents for the first time split Bregman total variation (TV) regularization to solve the MIT inverse problem. Comparative evaluations are presented between proposed TV algorithm and more commonly used Tikhonov regularization method. Tikhonov regularization, which is based on the l ₂ -norm, is solved linearly, while TV is solved using the split Bregman formulation, which has been shown to be optimal for l ₁ -norm regularization. Experimental results are quantified by a number of image quality measurements, which show the superiority of the proposed TV method both on low conductivity and high conductivity MIT data. Significant improvement in MIT imaging results will make the proposed TV method a great candidate for both types of MIT imaging.

Sense of touch is a major part of man’s communication with their environment. Artificial skins can help robots to have the same sense of touch, especially for their social interactions. This paper presents a pressure mapping sensing using piezo-resistive fabric to represent aspects of the sense of touch. In past few years’ electrical impedance tomography (EIT) is considered to be able offer a good alternative for artificial skin in particular for its ease of adaptation for large area skin compared to individual matrix based sensors. The EIT has also very good temporal performance in data collection allowing for monitoring of fast responses to touch stimulation, enabling a truly real time touch sensing. Electromechanical responses of a conductive fabric can be exploited using EIT to create a low cost and large area touch sensing. Such electromechanical properties are often very complex, so to improve the imaging resolution and touch visibility an artificial intelligent (AI) was used in addition to the state of the art spatio-temporal imaging algorithm. This work demonstrates a step towards an integrated seamless skin with large area sensing in dynamical settings, closer to natural human skin’s behaviour. For the first time a dynamical touch sensing are studies by means of a spatio-temporal based electrical impedance tomography (EIT) imaging on a conductive fabric. The experimental results demonstrated the successful results by a combined AI with dynamical EIT imaging results in single and multiple points of touch.

Medical image analysis is increasingly providing a sophisticated set of tools for processing measurement inputs into clinically relevant outputs, although this is, on the whole, completed without consideration of the underlying physiology. In contrast, physiological modelling provides a predictive tool based on a physical and biological understanding of the underlying processes. In this editorial, we discuss the possibility of integrating physiological modelling data with medical images and measurements with the goal of providing new types of physiologically meaningful information with increased clinical relevance.