MagTetris: A simulator for fast magnetic field and force calculation for permanent magnet array designs.

In this paper, a simulator named "MagTetris" is proposed for fast magnetic field (B-field) and force calculation for permanent magnet arrays (PMAs) designs consisting of cuboid and arc-shaped magnets (approximated by cuboids) with arbitrary configurations. The proposed simulator can compute the B-field of a PMA on arbitrary observation planes and the magnetic force acting on any magnet/group of magnets. An accelerated calculation method for B-fields of PMAs is developed based on the current model of permanent magnet, which is further extended to magnetic force calculation. The proposed method and the associated codes were validated with numerical simulation and experimental results. The calculation speed of "MagTetris" is at least 500 times higher than that using finite-element method (FEM)-based software with uncompromised accuracy. Compared with a freeware in Python, Magpylib, "MagTetris" has a calculation acceleration of greater than 50% using the same language. "MagTetris" has a simple data structure, which can be easily migrated to other programming languages maintaining similar performances. This proposed simulator can accelerate a PMA design and/or allow designs with high flexibility considering the B-field and force simultaneously. It can facilitate and accelerate innovations of magnet designs to advance dedicated portable MRI in terms of compactness, weight, and performance.

High-performance permanent magnet array design by a fast genetic algorithm (GA)-based optimization for low-field portable MRI.

Lightweight and compact permanent magnet arrays (PMAs) are suitable for portable dedicated magnetic resonance imaging (MRI). It is worth exploring different PMA design possibilities and optimization methods with an adequate balance between weight, size, and performance, in addition to Halbach arrays and C-shaped/H-shaped magnets which are widely used. In this paper, the design and optimization of a sparse high-performance inward-outward ring-pair PMA consisting of magnet cuboids is presented for portable imaging of the brain. The design is lightweight (151kg) and compact (inner bore diameter: 270mm, outer diameter: 616mm, length: 480mm, 5-Gauss range: 1840×1840×2340mm3). The optimization framework is based on the genetic algorithm with a consideration of both field properties and simulated image quality. The resulting PMA design has an average field strength of 101.5 mT and a field pattern with a built-in linear readout gradient. Subtracting the best fit to the linear gradient target resulted in a residual deviation from the target field of 0.76mT and an average linear regression coefficient of 0.85 to the linear gradient. The required radiofrequency bandwidth is 6.9% within a field of view (FoV) with a diameter of 200mm and a length of 125mm. It has a magnetic field generation efficiency of 0.67mT/kg, which is high among the sparse PMAs that were designed for an FoV with a diameter of 200mm. The field can be used to supply gradients in one direction working with gradient coils in the other two directions, or can be rotated to encode signals for imaging with axial slice selection. The encoding capability of the designed PMA was examined through the simulated reconstructed images. The force experienced by each magnet in the design was calculated, and the feasibility of a physical implementation was confirmed. The design can offer an increased field strength, and thus, an increased signal-to-noise ratio. It has a longitudinal field direction that allows the application of technologies developed for solenoidal magnets. This proposed design can be a promising alternative to supplying the main and gradient fields in combination for dedicated portable MRI. Lastly, the design is resulted from a fast genetic algorithm-based optimization in which fast magnetic field calculation was applied and high design flexibility was feasible. Within optimization iterations, image quality metrics were used for the encoding field of a magnet configuration to guide the design of the magnet array.

Physics Informed Neural Networks (PINN) for Low Snr Magnetic Resonance Electrical Properties Tomography (MREPT).

Electrical properties (EPs) of tissues facilitate early detection of cancerous tissues. Magnetic resonance electrical properties tomography (MREPT) is a technique to non-invasively probe the EPs of tissues from MRI measurements. Most MREPT methods rely on numerical differentiation (ND) to solve partial differential Equations (PDEs) to reconstruct the EPs. However, they are not practical for clinical data because ND is noise sensitive and the MRI measurements for MREPT are noisy in nature. Recently, Physics informed neural networks (PINNs) have been introduced to solve PDEs by substituting ND with automatic differentiation (AD). To the best of our knowledge, it has not been applied to MREPT due to the challenges in using PINN on MREPT as (i) a PINN requires part of ground-truth EPs as collocation points to optimize the network's AD, (ii) the noisy input data disrupts the optimization of PINNs despite the noise-filtering nature of NNs and additional denoising processes. In this work, we propose a PINN-MREPT model based on a canonical analytic MREPT model. A reference padding layer with known EPs was added to surround the region of interest for providing additive collocation points. Moreover, an optimizable diffusion coefficient was embedded in the analytic MREPT model used in the PINN-MREPT. The noise robustness of the proposed PINN-MREPT for single-sample reconstruction was tested by using numerical phantoms of human brain with extra tumor-like tissues at different noise levels. The results of numerical experiments show that PINN-MREPT outperforms two typical numerical MREPT methods in terms of reconstruction accuracy, sensitivity to the extra tissues, and the correlations of line profiles in the regions of interest. The advantage of the PINN-MREPT is shown by the results of an experiment on phantom measurement, too. Moreover, it is found that the diffusion term plays an important role to achieve a noise-robust PINN-MREPT. This is an important step moving forward to a clinical application of MREPT.

Physics-Coupled Neural Network Magnetic Resonance Electrical Property Tomography (MREPT) for Conductivity Reconstruction.

The electrical property (EP) of human tissues is a quantitative biomarker that facilitates early diagnosis of cancerous tissues. Magnetic resonance electrical properties tomography (MREPT) is an imaging modality that reconstructs EPs by the radio-frequency field in an MRI system. MREPT reconstructs EPs by solving analytic models numerically based on Maxwell's equations. Most MREPT methods suffer from artifacts caused by inaccuracy of the hypotheses behind the models, and/or numerical errors. These artifacts can be mitigated by adding coefficients to stabilize the models, however, the selection of such coefficient has been empirical, which limit its medical application. Alternatively, end-to-end Neural networks-based MREPT (NN-MREPT) learns to reconstruct the EPs from training samples, circumventing Maxwell's equations. However, due to its pattern-matching nature, it is difficult for NN-MREPT to produce accurate reconstructions for new samples. In this work, we proposed a physics-coupled NN for MREPT (PCNN-MREPT), in which an analytic model, cr-MREPT, works with diffusion and convection coefficients, learned by NNs from the difference between the reconstructed and ground-truth EPs to reduce artifacts. With two simulated datasets, three generalization experiments in which test samples deviate gradually from the training samples, and one noise-robustness experiment were conducted. The results show that the proposed PCNN-MREPT achieves higher accuracy than two representative analytic methods. Moreover, compared with an end-to-end NN-MREPT, the proposed method attained higher accuracy in two critical generalization tests. This is an important step to practical MREPT medical diagnoses.

Effect of changes in Skin Thickness on pain-relief Transcutaneous Electrical Nerve Stimulation (TENS).

Transcutaneous Electrical Nerve Stimulation (TENS) suppresses chronic pain by stimulating deep nerves near the fascia from electrodes on the skin's surface. TENS has different effects on patients of different ages due to the variation of the thickness of skin layers when one becomes older.In this paper, we aim to optimize the stimulation effectiveness of TENS for patients of different ages through investigation of TENS stimulations of three different skin types categorized by age, Young, Old, and Older. In this investigation, the skin layer (stratum corneum, epidermis layer, dermis layer) in each model was created, and the thickness was varied. The effect of sin wave stimulation at 1 Hz, 100 Hz, and 10 kHz on the nerve stimulation effect near the fascia was examined.It is found that besides the well-known effect of stratum corneum, the thickness of the dermis layer significantly affects the stimulating effect. In addition, by using a lumped circuit model, it is showed that the change in the current path causes a mitigation in the stimulation effect in the dermis layer.

Gate Mechanism and Parameter Analysis of Anodal-First Waveforms for Improving Selectivity of C-Fiber Nerves.

PURPOSE: Few investigations have been conducted on the selective stimulation of small-radius unmyelinated C nerves (C), which are critical to both the recovery of damaged nerves and pain suppression. The purpose of this study is to understand how an anodal pulse in an anodal-first stimulation could improve C-selectivity over myelinated nociceptive Aδ nerves (Aδ) and to further clarify the landscape of the solution space. MATERIALS AND METHODS: An adapted Hodgkin-Huxley (HH) model and the McIntyre-Richardson-Grill (MRG) model were used for modeling C and Aδ, respectively, to analyze the underlying ion dynamics and the influence of relevant stimulation waveforms, including monopolar, polarity-symmetric, and asymmetric pulses. RESULTS: The results showed that polarity asymmetric waveforms with preceding anodal stimulations benefit C-selectivity the most, underlain by the decrease in the potassium ion current of C. CONCLUSION: The optimal parameters for C-selectivity have been identified in the low-frequency band, remarkably benefiting the design of selective stimulation waveforms for the recovery of damaged nerves and pain management.

Ultra-Deformable and Tissue-Adhesive Liquid Metal Antennas with High Wireless Powering Efficiency.

Flexible and stretchable antennas are important for wireless communication using wearable and implantable devices to address mechanical mismatch at the tissue-device interface. Emerging technologies of liquid-metal-based stretchable electronics are promising approaches to improve the flexibility and stretchability of conventional metal-based antennas. However, existing methods to encapsulate liquid metals require monolithically thick (at least 100 µm) substrates, and the resulting devices are limited in deformability and tissue-adhesiveness. To overcome this limitation, fabrication of microchannels by direct ink writing on a 7 µm-thick elastomeric substrate is demonstrated, to obtain liquid metal microfluidic antennas with unprecedented deformability. The fabricated wireless light-emitting device is powered by a standard near-field-communication system (13.56 MHz, 1 W) and retained a consistent operation under deformations including stretching (>200% uniaxial strain), twisting (180° twist), and bending (3.0 mm radius of curvature) while maintaining a high quality factor (q > 20). Suture-free conformal adhesion of the polydopamine-coated device to ex vivo animal tissues under mechanical deformations is also demonstrated. This technology offers a new capability for the design and fabrication of wireless biomedical devices requiring conformable tissue-device integration toward minimally invasive, imperceptible medical treatments.

A Review on Reconfigurable Liquid Dielectric Antennas.

The advancements in wireless communication impose a growing range of demands on the antennas performance, requiring multiple functionalities to be present in a single device. To satisfy these different application needs within a limited space, reconfigurable antennas are often used which are able to switch between a number of states, providing multiple functions using a single antenna. Electronic switching components, such as PIN diodes, radio-frequency micromechanical systems (RF-MEMS), and varactors, are typically used to achieve antenna reconfiguration. However, some of these approaches have certain limitations, such as narrow bandwidth, complex biasing circuitry, and high activation voltages. In recent years, an alternative approach using liquid dielectric materials for antenna reconfiguration has drawn significant attention. The intrinsic conformability of liquid dielectric materials allows us to realize antennas with desired reconfigurations with different physical constraints while maintaining high radiation efficiency. The purpose of this review is to summarize different approaches proposed in the literature for the liquid dielectric reconfigurable antennas. It facilitates the understanding of the advantages and limitations of this technology, and it helps to draw general design principals for the development of reconfigurable antennas in this category.

Classification of Pain Event Related Potential for Evaluation of Pain Perception Induced by Electrical Stimulation.

Variability in individual pain sensitivity is a major problem in pain assessment. There have been studies reported using pain-event related potential (pain-ERP) for evaluating pain perception. However, none of them has achieved high accuracy in estimating multiple pain perception levels. A major reason lies in the lack of investigation of feature extraction. The goal of this study is to assess four different pain perception levels through classification of pain-ERP, elicited by transcutaneous electrical stimulation on healthy subjects. Nonlinear methods: Higuchi's fractal dimension, Grassberger-Procaccia correlation dimension, with auto-correlation, and moving variance functions were introduced into the feature extraction. Fisher score was used to select the most discriminative channels and features. As a result, the correlation dimension with a moving variance without channel selection achieved the best accuracies of 100% for both the two-level and the three-level classification but degraded to 75% for the four-level classification. The best combined feature group is the variance-based one, which achieved accuracy of 87.5% and 100% for the four-level and three-level classification, respectively. Moreover, the features extracted from less than 20 trials could not achieve sensible accuracy, which makes it difficult for an instantaneous pain perception levels evaluation. These results show strong evidence on the possibility of objective pain assessment using nonlinear feature-based classification of pain-ERP.

An MR-Based Viscosity-Type Regularization Method for Electrical Property Tomography.

Here, a method based on viscosity-type regularization is proposed for magnetic resonance electrical property tomography (MREPT) to mitigate persistent artifacts when it is used to reconstruct a map of electrical properties based on data from a magnetic resonance imaging scanner. The challenges for solving the corresponding partial differential equation (PDE) are discussed in detail. The existing artifacts in the numerical results are pointed out and classified. The methods in the literature for MREPT are mainly based on an assumption of local homogeneity, which makes the approach simple but leads to artifacts in the transition region where electrical properties vary rapidly. Recent work has focused on eliminating the assumption of local homogeneity, and one of the solutions is convection-reaction MREPT that is based on a first-order PDE. Numerical solutions of the PDE have persistent artifacts in certain regions and global spurious oscillations. Here, a method based on viscosity-type regularization is proposed to effectively mitigate the aforementioned problems. Finite difference method is used for discretizing the governing PDE. Numerical experiments are presented to analyze the problem in detail. Electrical properties of different phantoms are successfully retrieved. The efficiency, accuracy, and noise tolerance of the proposed method are illustrated with numerical results.

Efficient Active Sensing with Categorized Further Explorations for a Home Behavior-Monitoring Robot.

Mobile robotics is a potential solution to home behavior monitoring for the elderly. For a mobile robot in the real world, there are several types of uncertainties for its perceptions, such as the ambiguity between a target object and the surrounding objects and occlusions by furniture. The problem could be more serious for a home behavior-monitoring system, which aims to accurately recognize the activity of a target person, in spite of these uncertainties. It detects irregularities and categorizes situations requiring further explorations, which strategically maximize the information needed for activity recognition while minimizing the costs. Two schemes of active sensing, based on two irregularity detections, namely, heuristic-based and template-matching-based irregularity detections, were implemented and examined for body contour-based activity recognition. Their time cost and accuracy in activity recognition were evaluated through experiments in both a controlled scenario and a home living scenario. Experiment results showed that the categorized further explorations guided the robot system to sense the target person actively. As a result, with the proposed approach, the robot system has achieved higher accuracy of activity recognition.

Clinical application of a new warfarin-dosing regimen based on the CYP2C9 and VKORC1 genotypes in atrial fibrillation patients.

The polymorphisms of cytochrome P450 2C9 (CYP2C9) and vitamin K epoxide reductase complex 1 (VKORC1) are important genetic factors for warfarin dose determinations. The present study aimed to investigate the contribution of the CYP2C9 and VKORC1 genotypes to warfarin dose requirement in atrial fibrillation (AF) patients, and to evaluate the clinical application of a warfarin-dosing algorithm. A total of 122 AF patients with a target international normalized ratio of 2.0 to 3.0 were included to determine the genotypes of CYP2C9 (rs1057910) and VKORC1 (rs9923231). A warfarin-dosing algorithm was developed based on age, height, and the CYP2C9 and VKORC1 genotypes of AF patients. The results indicated that the mean warfarin daily dose requirement was lower in the CYP2C9*1/*3 genotype compared with those in the homozygous wild-type CYP2C9*1/*1 patients (P<0.05), and was higher in patients with the VKORC1 AG and GG genotypes compared with those with the AA genotype (P<0.05). The multivariate regression model showed that age, height, and the CYP2C9 and VKORC1 genotypes were the best variables for estimating warfarin dose (R2=56.4%). A new warfarin-dosing algorithm was developed and its validity was confirmed in a second cohort of AF patients. During the 50-day follow-up, 63.3% (19/30) of control group patients and 86.7% (26/30) of patients in the experimental group acquired the warfarin maintenance dose. Among all the patients who acquired the warfarin maintenance dose, the mean time elapse from initiation until warfarin maintenance dose was significantly less in the experimental group (25.8±1.7 day) compared to the control group (33.1±1.9 day) (P<0.05). There was significant linear correlation between predicted warfarin maintenance dose and actual dose (r=0.822, P<0.01). In conclusion, a new warfarin-dosing algorithm was developed based on the CYP2C9 and VKORC1 genotypes, and it can shorten the time elapse from initiation until warfarin maintenance dose in AF patients with warfarin therapy.