An effect of complex permeability of noise suppression sheets (NSS) on circuit parameters was investigated by a magnetic circuit analysis using cross-sectional size and material parameters. The series resistance and inductance of the coplanar waveguide (CPW) with a NSS considering the effect of the complex permeability of the NSS were quantitatively estimated. The result indicated that the imaginary and real part of the effective permeability affected the resistance and inductance, respectively. Furthermore, this analysis was applied to an 8-µm-wide CPW with a 0.5-µm-thick Co85Zr3Nb12 film for quantitative estimation of the resistance, the inductance and the characteristic impedance. The estimated parameters were almost similar to the measured values. These results showed that the frequency characteristics of the circuit parameters could be controlled by changing size and material parameters.

Wireless power transfer (WPT) can be classified into magnetic induction, magnetic resonance, and RF radiation types, of which the magnetic resonance WPT system is especially attracting attention due to its high potential for development. The magnetic resonance system using a specific resonance frequency is applicable to small mobile devices and in-body wireless charging modules because it enables the implementation of single-input multiple-output (SIMO), where the transmitter transmits power to multiple receivers, and the miniaturization of receiving coil. The most important consideration of the magnetic resonance WPT is the optimization of the power transfer distance and efficiency, which requires a precise design and the analysis of the transmission coil. Ferrite-embedded LTCC inductors are more advantageous for WPT applications than coil inductors because of their low cost, batch manufacturing and durability. A coil with the substate size of 10.0×12.0×0.7mm3 was manufactured using the ferrite-embedded LTCC technology to miniaturize the receiver coil. The sum of power transferred from transmitter sized of 80×60mm2 to two receivers is approximately 32%, which indicates a high potential for use in small terminals or in-body modules.

In this paper, we propose a novel framework for structural magnetic resonance image (sMRI) classification of Alzheimer's disease (AD) with data combination, outlier removal, and entropy-based data selection using AlexNet. In order to overcome problems of conventional classical machine learning methods, the AlexNet classifier, with a deep learning architecture, was employed for training and classification. A data permutation scheme including slice integration, outlier removal, and entropy-based sMRI slice selection is proposed to utilize the benefits of AlexNet. Experimental results show that the proposed framework can effectively utilize the AlexNet with the proposed data permutation scheme by significantly improving overall classification accuracies for AD classification. The proposed method achieves 95.35% and 98.74% classification accuracies on the OASIS and ADNI datasets, respectively, for the binary classification of AD and Normal Control (NC), and also achieves 98.06% accuracy for the ternary classification of AD, NC, and Mild Cognitive Impairment (MCI) on the ADNI dataset. The proposed method can attain significantly improved accuracy of up to 18.15%, compared to previously developed methods.

This paper presents a capacitor-loaded 4x4 planar loop array for three-dimensional near-field beamforming of magnetic resonance wireless power transfer (WPT). This planar loop array provides three important functions: beamforming, selective power transfer, and the ability to work alignment free with the receiver. These functions are realized by adjusting the capacitance of each loop. The optimal capacitance of each loop that corresponds to the three functions can be found using a genetic algorithm (GA); the three functions were verified by comparing simulations and measurements at a frequency of 6.78MHz. Finally, the beamforming mechanism of a near-field loop array was investigated using the relationship between the current magnitude and the resonance frequency of each loop, resulting in the findings that the magnitude and the resonance frequency are correlated. This focused current of the specified loop creates a strong magnetic field in front of that loop, resulting in near-field beamforming.

This study proposes a novel resonator design that uses tightly coupled parallel coils to improve the quality factor (Q factor) in coupled magnetic resonance wireless power transfer. Depending on the characteristics of the tightly coupled parallel-connected coils, the proposed resonator can offer significantly reduced resistance with very little self-inductance loss. A double-layer spiral coil structure is used for resonator design and evaluating its characteristics. Measured results show that a resonator consisting of two identical, tightly coupled parallel double-layer spiral coils can match the Q factor of a conventional double-layer spiral coil with the same number of turns, even though its equivalent resistance is approximately 75% less. Moreover, the system power transfer performance of the resonator was measured under the impedance matching condition. To further reduce the resistance, we propose another resonator comprising of three parallel and tightly coupled double-layer spiral coils, and measure its equivalent resistance characteristics for different wire gap sizes.

This paper presents a nonlinear model of human brain activity in response to visual stimuli according to Blood-Oxygen-Level-Dependent (BOLD) signals scanned by functional Magnetic Resonance Imaging (fMRI). A BOLD signal often contains a low frequency signal component (trend), which is usually removed by detrending because it is considered a part of noise. However, such detrending could destroy the dynamics of the BOLD signal and ignore an essential component in the response. This paper shows a model that, in the absence of detrending, can predict the BOLD signal with smaller errors than existing models. The presented model also has low Schwarz information criterion, which implies that it will be less likely to overfit the experimental data. Comparison between the various types of artificial trends suggests that the trends are not merely the result of noise in the BOLD signal.

To determine the thickness from MR images, segmentation, that is, boundary detection, of the two adjacent thin structures (e.g., femoral cartilage and acetabular cartilage in the hip joint) is needed before thickness determination. Traditional techniques such as zero-crossings of the second derivatives are not suitable for the detection of these boundaries. A theoretical simulation analysis reveals that the zero-crossing method yields considerable biases in boundary detection and thickness measurement of the two adjacent thin structures from MR images. This paper studies the accurate detection of hip cartilage boundaries in the image plane, and a new method based on a model of the MR imaging process is proposed for this application. Based on the newly developed model, a hip cartilage boundary detection algorithm is developed. The in-plane thickness is computed based on the boundaries detected using the proposed algorithm. In order to correct the image plane thickness for overestimation due to oblique slicing, a three-dimensional (3-D) thickness computation approach is introduced. Experimental results show that the thickness measurement obtained by the new thickness computation approach is more accurate than that obtained by the existing thickness computation approaches.

Arterial spin labeling (ASL) is a non-invasive magnetic resonance imaging (MRI) method that can provide direct and quantitative measurements of cerebral blood flow (CBF) of scanned patients. ASL can be utilized as an imaging modality to detect Alzheimer's disease (AD), as brain atrophy of AD patients can be revealed by low CBF values in certain brain regions. However, partial volume effects (PVE), which is mainly caused by signal cross-contamination due to voxel heterogeneity and limited spatial resolution of ASL images, often prevents CBF in ASL from being precisely measured. In this study, a novel PVE correction method is proposed based on pixel-wise voxels in ASL images; it can well handle with the existing problems of blurring and loss of brain details in conventional PVE correction methods. Dozens of comparison experiments and statistical analysis also suggest that the proposed method is superior to other PVE correction methods in AD diagnosis based on real patients data.

In recent years, magnetic resonance imaging (MRI) systems that operate up to under 3T are being used in clinical practice in Japan. In order to achieve the requirements of higher image quality and shorter imaging times, devices that utilize high magnetic fields (> 3T) and high power electromagnetic (EM) wave pulses have been developed. The rise of the static magnetic field is proportional to the increase of the EM wave frequency which raises the issue of variation in capacitance used in the radio frequency (RF) coil for MRI system. In addition, increasing power causes problems of withstanding voltage and these approaches leads to generation of non-uniform magnetic field inside the RF coil. Therefore, we proposed a birdcage coil without the use of lumped circuit elements for MRI systems in previous study. However, it is difficult to fabricate this birdcage coil. Hence, simply-structured birdcage coil with no lumped circuit elements is desired. In this paper, we propose a simply-structured birdcage coil with no lumped circuit elements for a 4T MRI system. In addition, the authors investigated the input impedance and magnetic field distribution of the proposed coil by FDTD calculations and measurements. The results confirm that the proposed birdcage coil matches the performance of the conventional birdcage coil which includes several capacitors.

Diffusion-weighted (DW)-functional magnetic resonance imaging (fMRI) is a recently reported technique for measuring neural activities by using diffusion-weighted imaging (DWI). DW-fMRI is based on the property that cortical cells swell when the brain is activated. This approach can be used to observe changes in water diffusion around cortical cells. The spatial and temporal resolutions of DW-fMRI are superior to those of blood-oxygenation-level-dependent (BOLD)-fMRI. To investigate how the DWI signal intensities change in DW-fMRI measurement, we carried out Monte Carlo simulations to evaluate the intensities before and after cell swelling. In the simulations, we modeled cortical cells as two compartments by considering differences between the intracellular and the extracellular regions. Simulation results suggested that DWI signal intensities increase after cell swelling because of an increase in the intracellular volume ratio. The simulation model with two compartments, which respectively represent the intracellular and the extracellular regions, shows that the differences in the DWI signal intensities depend on the ratio of the intracellular and the extracellular volumes. We also investigated the MPG parameters, b-value, and separation time dependences on the percent signal changes in DW-fMRI and obtained useful results for DW-fMRI measurements.

As sensitive magnetic sensors, magnetometers based on superconducting quantum interference devices can be used for the detection of weak magnetic fields. These signals can be generated by diverse origins, for example, brain electric activity, myocardium electric activity, and nuclear precession of hydrogen protons. In addition, weak current induced in the low-temperature detectors, for example, transition-edge sensors can be detected using SQUIDs. And, change of magnetic flux quantum generated in a superconducting ring can be detected by SQUID, which can be used for realization of mechanical force. Thus, SQUIDs are key elements in precision metrology. In Korea, development of low-temperature SQUIDs based on Nb-Josephson junctions was started in late 1980s, and Nb-based SQUIDs have been used mainly for biomagnetic measurements; magnetocardiography and magnetoencephalography. High-Tc SQUIDs, being developed in mid 1990s, were used for magnetocardiography and nondestructive evaluation. Recently, SQUID-based low-field nuclear magnetic resonance technology is under development. In this paper, we review the past progress and recent activity of SQUID applications in Korea, with focus on biomagnetic measurements.

We propose an active noise control (ANC) system for reducing periodic noise generated in a high magnetic field such as noise generated from magnetic resonance imaging (MRI) devices (MR noise). The proposed ANC system utilizes optical microphones and piezoelectric loudspeakers, because specific acoustic equipment is required to overcome the high-field problem, and consists of a head-mounted structure to control noise near the user's ears and to compensate for the low output of the piezoelectric loudspeaker. Moreover, internal model control (IMC)-based feedback ANC is employed because the MR noise includes some periodic components and is predictable. Our experimental results demonstrate that the proposed ANC system (head-mounted structure) can significantly reduce MR noise by approximately 30 dB in a high field in an actual MRI room even if the imaging mode changes frequently.

This paper presents the computational electromagnetic dosimetry inside an anatomically based pregnant woman models exposed to electromagnetic wave during magnetic resonance imaging. The two types of pregnant woman models corresponding to early gestation and 26 weeks gestation were used for this study. The specific absorption rate (SAR) in and around a fetus were calculated by radiated electromagnetic wave from highpass and lowpass birdcage coil. Numerical calculation results showed that high SAR region is observed at the body in the vicinity of gaps of the coil, and is related to concentrated electric field in the gaps of human body such as armpit and thigh. Moreover, it has confirmed that the SAR in the fetus is less than International Electrotechnical Commission limit of 10 W/kg, when whole-body average SARs are 2 W/kg and 4 W/kg, which are the normal operating mode and first level controlled operating mode, respectively.

Finding intracranial aneurysms plays a key role in preventing serious cerebral diseases such as subarachnoid hemorrhage. For detection of aneurysms, magnetic resonance angiography (MRA) can provide detailed images of arteries non-invasively. However, because over 100 MRA images per subject are required to cover the entire cerebrum, image diagnosis using MRA is very time-consuming and labor-intensive. This article presents a computer-aided diagnosis (CAD) system for finding aneurysms with MRA images. The principal components are identification of aneurysm candidates (= ROIs; regions of interest) from MRA images and estimation of a fuzzy degree for each aneurysm candidate based on a case-based reasoning (CBR). The fuzzy degree indicates whether a candidate is true aneurysm. Our system presents users with a limited number of ROIs that have been sorted in order of fuzzy degree. Thus, this system can decrease the time and the labor required for detecting aneurysms. Experimental results using phantoms indicate that the system can detect all aneurysms at branches of arteries and all saccular aneurysms produced by dilation of a straight artery in 1 direction perpendicular to the principal axis. In a clinical evaluation, performance in finding aneurysms and estimating the fuzzy degree was examined by applying the system to 16 subjects with a total of 19 aneurysms. The experimental results indicate that this CAD system detected all aneurysms except a fusiform aneurysm, and gave high fuzzy degrees and high priorities for the detected aneurysms.

An efficient algorithm to reduce the noise from the Nuclear Magnetic Resonance Free Induction Decay (NMR FID) signals is presented, in this paper, via the oversampled real-valued discrete Gabor transform using the Gaussian synthesis window. An NMR FID signal in the Gabor transform domain (i.e., a joint time-frequency domain) is concentrated in a few number of Gabor transform coefficients while the noise is fairly distributed among all the coefficients. Therefore, the NMR FID signal can be significantly enhanced by performing a thresholding technique on the coefficients in the transform domain. Theoretical and simulation experimental analyses in this paper show that the oversampled Gabor transform using the Gaussian synthesis window is more suitable for the NMR FID signal enhancement than the critically-sampled one using the exponential synthesis window, because both the Gaussian synthesis window and its corresponding analysis window in the oversampling case can have better localization in the frequency domain than the exponential synthesis window and its corresponding analysis window in the critically-sampling case. Moreover, to speed up the transform, instead of the commonly-used complex-valued discrete Gabor transform, the real-valued discrete Gabor transform presented in our previous work is adopted in the proposed algorithm.

Recent investigations using magnetic resonance imaging (MRI) of human speech organs have opened up new avenues of research. Visualization of the speech production system provides abundant information on the physiological and acoustic realization of human speech. This article summarizes the current status of MRI applications with respect to speech research as well as our own experience of discovery and re-evaluation of acoustic events emanating from the vocal tract and physiological mechanisms.

A novel technique for automatic segmentation of a brain region in single channel MR images for visualization and analysis of a human brain is presented. The method generates a volume of brain masks by automatic thresholding using a dual curve fitting technique and by 3D morphological operations. The dual curve fitting can reduce an error in curve fitting to the histogram of MR images. The 3D morphological operations, including erosion, labeling of connected-components, max-feature operation, and dilation, are applied to the cubic volume of masks reconstructed from the thresholded brain masks. This method can automatically segment a brain region in any displayed type of sequences, including extreme slices, of SPGR, T1-, T2-, and PD-weighted MR image data sets which are not required to contain the entire brain. In the experiments, the algorithm was applied to 20 sets of MR images and showed over 0.97 of similarity index in comparison with manual drawing.

Functional magnetic resonance imaging (fMRI) allows to display functional activities of certain brain areas. In combination with a three dimensional anatomical dataset, acquired with a standard magnetic resonance (MR) scanner, it can be used to identify eloquent brain areas, resulting in so-called functional neuronavigation, supporting the neurosurgeon while planning and performing the operation. But during the operation brain shift leads to an increasing inaccuracy of the navigation system. Intraoperative MR imaging is used to update the neuronavigation system with a new anatomical dataset. To preserve the advantages of functional neuronavigation, it is necessary to save the functional information. Since fMRI cannot be repeated intraoperatively with the unconscious patient easily we tried to solve this problem by means of image processing and pattern recognition algorithms. In this paper we present an automatic approach for transfering preoperative markers into an intraoperative 3-D dataset. In the first step the brains are segmented in both image sets which are then registered and aligned. Next, corresponding points are determined. These points are then used to determine the position of the markers by estimating the local influence of brain shift.

When the inter-slice resolution of tomographic image slices is large, it is necessary to estimate the locations and intensities of pixels, which would appear in the non-existed intermediate slices. This paper presents a new method for generating the missing medical slices from two given slices. It uses the contours of organs as the control parameters to the intensity information in the physical gaps of sequential medical slices. The Snake model is used for generating the control points required for the elastic body spline (EBS) morphing algorithm. Contour information derived from this segmentation pre-process is then further processed and used as control parameters to warp the corresponding regions in both input slices into compatible shapes. In this way, the intensity information of the interpolated intermediate slices can be derived more faithfully. In comparison with the existing intensity interpolation methods, including linear interpolation, which only considers corresponding points in a small physical neighborhood, this method warps the data images into similar shapes according to contour information to provide a more meaningful correspondence relationship.

This paper describes the application of an unsupervised parallel approach called the Annealed Hopfield Neural Network (AHNN) using a modified cost function with moment and entropy preservation for magnetic resonance image (MRI) classification. In the AHNN, the neural network architecture is same as the original 2-D Hopfield net. And a new cooling schedule is embedded in order to make the modified energy function to converge to an equilibrium state. The idea is to formulate a clustering problem where the criterion for the optimum classification is chosen as the minimization of the Euclidean distance between training vectors and cluster-center vectors. In this article, the intensity of a pixel in an original image, the first moment combined with its neighbors, and their gray-level entropy are used to construct a 3-component training vector to map a neuron into a two-dimensional annealed Hopfield net. Although the simulated annealing method can yield the global minimum, it is very time-consuming with asymptotic iterations. In addition, to resolve the optimal problem using Hopfield or simulated annealing neural networks, the weighting factors to combine the penalty terms must be determined. The quality of final result is very sensitive to these weighting factors, and feasible values for them are difficult to find. Using the AHNN for magnetic resonance image classification, the need of finding weighting factors in the energy function can be eliminated and the rate of convergence is much faster than that of simulated annealing. The experimental results show that better and more valid solutions can be obtained using the AHNN than the previous approach in classification of the computer generated images. Promising solutions of MRI segmentation can be obtained using the proposed method. In addition, the convergence rates with different cooling schedules in the test phantom will be discussed.