Technologies for magnetic induction tomography sensors and image reconstruction in medical assisted diagnosis: A review

State-restricted adaptive control of a multilevel rotating electromagnetic mechanical flexible device using electromagnetic actuators

This work presents the development of a multilevel electromagnetic actuation system that controls the shape of a flexible rotatory robotic structure. An array of electromagnets is used as the set of actuators that regulate the position of permanent magnets within the flexible device. The primary outcome of this study is the design and experimental validation of the multilevel rotating device. In addition, the theoretical description of the system motion under electromagnetic actuation is formulated using Euler-Lagrange and electromagnetic theories. Given the developed model, a theoretical study leads to designing an adaptive control that considers motion restrictions in the flexible device. The controller aims to modify the current applied to the electromagnets, which changes the interaction forces between the electromagnet and the permanent magnets in the robotic flexible structure. A set of numerical simulations confirms the proposed controller's effectiveness compared to the traditional state feedback approach that does not consider the state restrictions, which is implemented in devices that also operate under an electromagnetic approach. Furthermore, an experimental version of the flexible device allows for testing of the developed controller. The experimental results show the suitability of the proposed control to generate non-oscillatory controlled motion during the regulation of the flexible mechanic device shape.

Biplane Enhancement Coil for Magnetic Induction Tomography of Cerebral Hemorrhage

Magnetic Induction Tomography (MIT) is a non-invasive imaging technique used for dynamic monitoring and early screening of cerebral hemorrhage. Currently, there is a significant challenge in cerebral hemorrhage MIT due to weak detection signals, which seriously affects the accuracy of the detection results. To address this issue, a dual-plane enhanced coil was proposed by combining the target field method with consideration of the spatial magnetic field attenuation pattern within the imaging target region. Simulated detection models were constructed using the proposed coil and cylindrical coil as excitation coils, respectively, and simulation imaging tests were conducted using the detection results. The simulation results indicate that compared to the cylindrical coil, the proposed coil enhances the linearity of the magnetic field within the imaging target region by 60.43%. Additionally, it effectively enhances the detection voltage and phase values. The simulation results of hemorrhage detection show that the proposed coil improves the accuracy of hemorrhage detection by 18.26%. It provides more precise detection results, offering a more reliable solution for cerebral hemorrhage localization and detection.

Magnetic Induction Tomography: Separation of the Ill-Posed and Non-Linear Inverse Problem into a Series of Isolated and Less Demanding Subproblems

Magnetic induction tomography (MIT) is based on remotely excited eddy currents inside a measurement object. The conductivity distribution shapes the eddies, and their secondary fields are detected and used to reconstruct the conductivities. While the forward problem from given conductivities to detected signals can be unambiguously simulated, the inverse problem from received signals back to searched conductivities is a non-linear ill-posed problem that compromises MIT and results in rather blurry imaging. An MIT inversion is commonly applied over the entire process (i.e., localized conductivities are directly determined from specific signal features), but this involves considerable computation. The present more theoretical work treats the inverse problem as a non-retroactive series of four individual subproblems, each one less difficult by itself. The decoupled tasks yield better insights and control and promote more efficient computation. The overall problem is divided into an ill-posed but linear problem for reconstructing eddy currents from given signals and a nonlinear but benign problem for reconstructing conductivities from given eddies. The separated approach is unsuitable for common and circular MIT designs, as it merely fits the data structure of a recently presented and planar 3D MIT realization for large biomedical phantoms. For this MIT scanner, in discretization, the number of unknown and independent eddy current elements reflects the number of ultimately searched conductivities. For clarity and better representation, representative 2D bodies are used here and measured at the depth of the 3D scanner. The overall difficulty is not substantially smaller or different than for 3D bodies. In summary, the linear problem from signals to eddies dominates the overall MIT performance.

Adaptive threshold split Bregman algorithm based on magnetic induction tomography for brain injury monitoring imaging

Objective. Traditional magnetic induction tomography (MIT) algorithms have problems in reconstruction, such as large area error (AE), blurred boundaries of reconstructed targets, and considerable image noise (IN). As the size and boundary of a lesion greatly affect the treatment plan, more accurate algorithms are necessary to meet clinical needs.Approach. In this study, adaptive threshold split Bregman (ATSB) is proposed for brain injury monitoring imaging in MIT. We established a 3D brain MIT simulation model with the actual anatomical structure and a phantom model and obtained the reconstructed images of single targets in different positions and multiple targets, using the Tikhonov, eigenvalue threshold regularisation (ETR), split Bregman (SB), and ATSB algorithms.Main results. Compared with the Tikhonov and ETR algorithms, the ATSB algorithm reduced the AE by 95% and the IN by 17% in a simulation and reduced the AE by 87% and IN by 6% in phantom experiments. Compared with the SB algorithm, the ATSB algorithm can reduce the difficulty of adjusting parameters and is easier to use in clinical practice. The simulation and phantom experiments results showed that the ATSB algorithm could reconstruct the target size more accurately and could distinguish multiple targets more effectively than the other three algorithms.Significance. The ATSB algorithm could improve the image quality of MIT and better meet the needs of clinical applications and is expected to promote brain injury monitoring imaging via MIT.

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.

High-resolution magnetic sensors in ferrite/piezoelectric heterostructure with giant magnetodielectric effect at zero bias field

A dielectric AC magnetic sensor in layered ferrites/piezoelectric composites was fabricated and developed, whereby its high magnetodielectric (MDE) effects, the typical magnetic-sensing parameters, were systematically characterized at zero bias. Polycrystalline ferrites were synthesized by the solid-state sintering technique with a composition of Ni_0.7Zn_0.3Tb_0.02Fe_1.98O₄, and the desired spinel structure and soft magnetic properties were confirmed by x-ray diffraction and VSM, respectively. The field-induced charge order insulating state in piezoelectric ceramics accounts for the suppressed permittivity, which enables the possibility of a highly sensitive magnetic sensor at zero bias field. Experimental results exhibit that a small variation in H as low as 100 mOe can be clearly distinguished with a favorable nonlinearity of 2.24%. Meanwhile, the output stability of the presented sensor under 2h of constant and continuous excitation was tested within a favorable fluctuating tolerance range of 6.14-6.28 nF, and the estimated uncertainty of ∼0.063 038 nF was verified by statistical analysis. The presented ferrite/piezoelectric magnetic sensors exhibiting a high MDE response without the requirement for an external magnetic bias are of importance for use in bio-magnetic field detection due to metrics of miniaturization, high sensitivity, and favorable stabilities.