A New Method of Haemorrhagic Stroke Detection Via Deep Magnetic Induction Tomography

Repetitive Transcranial Magnetic Stimulation of the Brain After Ischemic Stroke: Mechanisms from Animal Models

Stroke is a common cerebrovascular disease with high morbidity, mortality, and disability worldwide. Post-stroke dysfunction is related to the death of neurons and impairment of synaptic structure, which results from cerebral ischemic damage. Currently, transcranial magnetic stimulation (TMS) techniques are available to provide clinically effective interventions and quantitative diagnostic and prognostic biomarkers. The development of TMS has been 40 years and a range of repetitive TMS (rTMS) protocols are now available to regulate neuronal plasticity in many neurological disorders, such as stroke, Parkinson disease, psychiatric disorders, Alzheimer disease, and so on. Basic studies in an animal model with ischemic stroke are significant for demonstrating potential mechanisms of neural restoration induced by rTMS. In this review, the mechanisms were summarized, involving synaptic plasticity, neural cell death, neurogenesis, immune response, and blood-brain barrier (BBB) disruption in vitro and vivo experiments with ischemic stroke models. Those findings can contribute to the understanding of how rTMS modulated function recovery and the exploration of novel therapeutic targets. The mechanisms of rTMS in treating ischemic stroke from animal models. rTMS can prompt synaptic plasticity by increasing NMDAR, AMPAR and BDNF expression; rTMS can inhibit pro-inflammatory cytokines TNF and facilitate the expression of anti-inflammatory cytokines IL-10 by shifting astrocytic phenotypes from A1 to A2, and shifting microglial phenotypes from M1 to M2; rTMS facilitated the release of angiogenesis-related factors TGFβ and VEGF in A2 astrocytes, which can contribute to vasculogenesis and angiogenesis; rTMS can suppress apoptosis by increasing Bcl-2 expression and inhibiting Bax, caspase-3 expression; rTMS can also suppress pyroptosis by decreasing caspase-1, IL-1β, ASC, GSDMD and NLRP1 expression. rTMS, repetitive transcranial magnetic stimulation; NMDAR, N-methyl-D-aspartic acid receptors; AMPAR: α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors; BDNF, brain-derived neurotrophic factor; VEGF, vascular endothelial growth factor; GSDMD: cleaved Caspase-1 cleaves Gasdermin D; CBF: cerebral blood flow.

Application of Particle Swarm Optimization with Simulated Annealing in MIT Regularization Image Reconstruction

Background and Objectives: Due to the soft-field effect of the electromagnetic field and the limit of detection, image reconstruction of magnetic induction tomography has to recover more complex electrical characteristics from very few signals. These cause a problem which have underdetermination, nonlinearity, and ill-posed characteristics, and therefore lead to many difficulties in finding its solution. Although many regularization image reconstruction methods exist, they are not suitable for MIT applications due to regularization parameter selection. The purpose of this paper is to study the principle of particle swarm optimization with simulated annealing, and to propose a regularization method for reconstruction, which will provide a new way to solve the MIT image problems. Methods and Models: Firstly, the regularization principle of image reconstruction of MIT will be analyzed. Then the hybrid regularization algorithm, including Tikhonov and NOSER regularization, will be developed, using the dimension of the Hessian matrix as a penalty term respecting the prior knowledge. PSO-SA algorithm will be applied to obtain an optimal solution for regularization parameters. Finally, six typical numerical models and approximately symmetrical cerebral hemorrhage models by COMSOL will be carried out, and the voltage signals obtained from the simulation will be used to verify the proposed reconstruction method. Results: Through the simulation results, the proposed imaging method has the average CC values of 0.9932, 0.8286 and the average RE values of 0.4982, 0.8320 for simple and complex models, respectively. Moreover, when the SNR changes from 55dB to 35dB, the CC value of the cerebral hemorrhage model reduced by 0.1034. The results demonstrate the effectiveness and high theoretical feasibility of the proposed method in MIT image reconstruction. Conclusions: This study indicates the potential application of PSO-SA algorithm in regularization imaging problem. Compared with traditional regularization imaging methods, the proposed method has the advantages of better accuracy, robustness and noise resistance, showing the certain application value in other similar ill-ness imaging problems.

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.

Urinary Bladder Volume Monitoring Using Magnetic Induction Tomography: A Rotational Simulation Model for Anatomical Slices within the Pelvic Region

Urinary bladder volume monitoring can benefit from contactless measurements, as alternative to the traditional medical methods of transurethral catheterization or ultrasound examination. The emerging modality of Magnetic Induction Tomography (MIT) offers the possibility for estimation of the intravesical volume in the physiological and pathological states using conductivity map reconstructions of the tissues present in the pelvic region. Within MIT, eddy currents originating from the conductive urine can produce their own magnetic field in response to an external magnetic source that is susceptible of being detected outside the body by means of a static ring of sensing coils. However, the ill-conditioned and ill-posed nature of the MIT Inverse Problem make the numerical implementation and conductivity estimation highly laborious. In this paper, we present a rotational frame model based on the MIT principles with application in urodynamic studies, which allows to extend the number of contactless measurements without increasing the overall dimension of the simulation domain, at the expense of solving multiple MIT Forward Problems. On the inversion process, the single-step Gauss-Newton method with Laplacian regularizer is recruited to estimate the bladder volume non-invasively and remotely (estimation error of 19%), paving the way for this technique to surpass the current limitations found in intravesical volume monitoring in quasi-real time.