Three-Dimensional Magnetic Induction Tomography: Practical Implementation for Imaging throughout the Depth of a Low Conductive and Voluminous Body

Magnetic induction tomography (MIT) is a contactless, low-energy method used to visualize the conductivity distribution inside a body under examination. A particularly demanding task is the three-dimensional (3D) imaging of voluminous bodies in the biomedical impedance regime. While successful MIT simulations have been reported for this regime, practical demonstration over the entire depth of weakly conductive bodies is technically difficult and has not yet been reported, particularly in terms of more realistic requirements. Poor sensitivity in the central regions critically affects the measurements. However, a recently simulated MIT scanner with a sinusoidal excitation field topology promises improved sensitivity (>20 dB) from the interior. On this basis, a large and fast 3D MIT scanner was practically realized in this study. Close agreement between theoretical forward calculations and experimental measurements underline the technical performance of the sensor system, and the previously only simulated progress is hereby confirmed. This allows 3D reconstructions from practical measurements to be presented over the entire depth of a voluminous body phantom with tissue-like conductivity and dimensions similar to a human torso. This feasibility demonstration takes MIT a step further toward the quick 3D mapping of a low conductive and voluminous object, for example, for rapid, harmless and contactless thorax or lung diagnostics.

Comparison of Algebraic Reconstruction Technique Methods and Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography (MIT)

Magnetic induction tomography (MIT) is a technique used for imaging electromagnetic properties of objects using eddy current effects. The non-linear characteristics had led to more difficulties with its solution especially in dealing with low conductivity imaging materials such as biological tissues. Two methods that could be applied for MIT image processing which is the Generative Adversarial Network (GAN) and the Algebraic Reconstruction Technique (ART). ART is widely used in the industry due to its ability to improve the quality of the reconstructed image at a high scanning speed. GAN is an intelligent method which would be able to carry out the training process. In the GAN method, the MIT principle is used to find the optimum global conductivity distribution and it is described as a training process and later, reconstructed by a generator. The output is an approximate reconstruction of the distribution’s internal conductivity image. Then, the results were compared with the previous traditional algorithm, namely the regularization algorithm of BPNN and Tikhonov Regularization method. It turned out that GAN had able to adjust the non-linear relationship between input and output. GAN was also able to solve non-linear problems that cannot be solved in the previous traditional algorithms, namely Back Propagation Neural Network (BPNN) and Tikhonov Regularization method. There are several other intelligent algorithms such as CNN (Convolution Neural Network) and K-NN (K-Nearest Neighbor), but such algorithms have not been able to produce the expected image quality. Thus, further study is still needed for the improvement of the image quality. The expected result in this study is the comparison of these two techniques, namely ART and GAN to get the best results on the image reconstruction using MIT. Thus, it is shown that GAN is a better candidate for this purpose.

Simulation of Single Channel Magnetic Induction Tomography for Meningitis Detection By Using COMSOL Multiphysics

Meningitis is a inflammation of the meninges and the most common central nervous system (CNS) due to bacterial infection. Numbers of children who have bacterial meningitis are still high in recent 15 years regardless of the availability of newer antibiotics and preventive strategies. This research focuses on simulation using COMSOL Multiphysics on the design and development of magnetic induction tomography (MIT) system that emphasizes on a single channel rotatable of brain tissue imaging. The purpose of this simulation is to test the capability of the developed MIT system in detecting the change in conductivity and to identify the suitable transmitter-receiver pair and the optimum frequency based on phase shift measurement technique for detecting the conductivity property distribution of brain tissues. The obtained result verified that the performance of the square coil with 12 number of turns (5Tx-12Rx) with 10MHz frequency has been identified as the suitable transmitter-receiver pair and the optimum frequency for detecting the conductivity property distribution of brain tissues.

A Study of Magnetic Induction Tomography (MIT) for Calcium Oxalate Renal Screening

Nephrolithiasis is a process of stone formation in the kidney by crystallization. The increasing prevalence of nephrolithiasis from time to time had sought an alternative from the conventional imaging techniques that is invasive, radiative, and non-rapid usage. This paper enclosed a design simulation study of Magnetic Induction Tomography (MIT) system using COMSOL Multiphysics for renal imaging. MIT is a soft field tomography and non-contact imaging modality which can project the passive electromagnetic properties (conductivity, permittivity and permeability) under the principle of electromagnetic induction. In this research also, 8 copper trans-receiver coils were employed in the MIT system and fixed by the insulation belt. Meanwhile, geometric set-up of renal organ was set to imitate the transverse section of human renal. In the methodology, sensor performance analyses were done using frequency ranging from 50 kHz to 2 MHz of the MIT system on radii of calcium oxalate in renal. The sensor response and pattern is discussed in this paper.

Application of a Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography

Image reconstruction of Magnetic induction tomography (MIT) is an ill-posed problem. The non-linear characteristics lead many difficulties to its solution. In this paper, a method based on a Generative Adversarial Network (GAN) is presented to tackle these barriers. Firstly, the principle of MIT is analyzed. Then the process for finding the global optimum of conductivity distribution is described as a training process, and the GAN model is proposed. Finally, the image was reconstructed by a part of the model (the generator). All datasets are obtained from an eight-channel MIT model by COMSOL Multiphysics software. The voltage measurement samples are used as input to the trained network, and its output is an estimate for image reconstruction of the internal conductivity distribution. The results based on the proposed model and the traditional algorithms were compared, which have shown that average root mean squared error of reconstruction results obtained by the proposed method is 0.090, and the average correlation coefficient with original images is 0.940, better than corresponding indicators of BPNN and Tikhonov regularization algorithms. Accordingly, the GAN algorithm was able to fit the non-linear relationship between input and output, and visual images also show that it solved the usual problems of artifact in traditional algorithm and hot pixels in L2 regularization, which is of great significance for other ill-posed or non-linear problems.

Multi-Frequency Magnetic Induction Tomography System and Algorithm for Imaging Metallic Objects

Magnetic induction tomography (MIT) is largely focused on applications in biomedical and industrial process engineering. MIT has a great potential for imaging metallic samples; however, there are fewer developments directed toward the testing and monitoring of metal components. Eddy-current non-destructive testing is well established, showing that corrosion, fatigue and mechanical loading are detectable in metals. Applying the same principles to MIT would provide a useful imaging tool for determining the condition of metal components. A compact MIT instrument is described, including the design aspects and system performance characterisation, assessing dynamic range and signal quality. The image rendering ability is assessed using both external and internal object inclusions. A multi-frequency MIT system has similar capabilities as transient based pulsed eddy current instruments. The forward model for frequency swap multi-frequency is solved, using a computationally efficient numerical modelling with the edge-based finite elements method. The image reconstruction for spectral imaging is done by adaptation of a spectrally correlative base algorithm, providing whole spectrum data for the conductivity or permeability.