A new approach to the solution of Maxwell's equations for low frequency and high-resolution biomedical problems

High spatial resolution studies of the interaction of the human body with electromagnetic waves of low frequency presents a difficult computational problem. As these studies typically require at least $10^4$ points per wavelength, a huge number of time steps would be needed to be able to use the finite difference time domain method (FDTD). In this paper, a new technique is described, which allows the FDTD method to be efficiently applied over a very large frequency range, including low frequencies. In the method, no alterations to the properties of either the source or the transmission media are required. The method is essentially frequency independent and has been verified against analytical solutions within the frequency range 50 Hertz to 1 Gigahertz. As an example of the lower frequency range, the method has been applied to the simulation of electromagnetic field behavior in the human body exposed to the pulsed magnetic field gradients of a magnetic resonance image (MRI) system

FDTD Investigations into UWB Radar Technique of Breast Tumor Detection and Location

In this paper, a finite difference time domain (FDTD) method is applied to investigate capabilities of an ultra-wide band (UWB) radar system to detect and locate a breast tumor. The investigations are divided into three parts. The first part concerns an EM field analysis of a phantom formed by a plastic container with liquid and a small highly reflecting target. In the second part, a three-dimensional numerical breast model is used to perform more advanced studies. In the carried out 3D FDTD simulations, a quasi-plane wave is used as an incident wave. Various time snap shots of the electromagnetic field are recorded to learn about the physical phenomenon of reflection and scattering in different layers of the phantoms. The third part of the investigations concerns a two dimensional (cylindrical) image reconstruction, which is performed by means of 2D FDTD. The obtained results should form the ground for working out suitable guidelines for designing an optimal microwave breast imaging apparatus based on the UWB radar technique

Ultra Wideband Transient Scattering and Its Applications to Automated Target Recognition

Reliable radar target recognition has long been the holy grail of electromagnetic sensors. Target recognition based on the singularity expansion method (SEM) uses a time-domain electromagnetic signature and has been well studied over the last few decades. The SEM describes the late time period of the transient target signature as a sum of damped exponentials with natural resonant frequencies (NRFs). The aspect-independent and purely target geometry and material-dependent nature of the NRF set make it an excellent feature set for target characterization. In this chapter, we aim to review the background and the state of the art of resonance-based target recognition. The theoretical framework of SEM is introduced, followed by signal processing techniques that retrieve the target-dependent NRFs embedded in the transient electromagnetic target signatures. The extinction pulse, a well-known target recognition technique, is discussed. This chapter covers recent developments in using a polarimetric signature for target recognition, as well as using NRFs for subsurface sensing applications. The chapter concludes with some highlights of the ongoing challenges in the field

Improved l1-SPIRiT using 3D walsh transform-based sparsity basis

l1-SPIRiT is a fast magnetic resonance imaging (MRI) method which combines parallel imaging (PI) with compressed sensing (CS) by performing a joint l1-norm and l2-norm optimization procedure. The original l1-SPIRiT method uses two-dimensional (2D) Wavelet transform to exploit the intra-coil data redundancies and a joint sparsity model to exploit the inter-coil data redundancies. In this work, we propose to stack all the coil images into a three-dimensional (3D) matrix, and then a novel 3D Walsh transform-based sparsity basis is applied to simultaneously reduce the intra-coil and inter-coil data redundancies. Both the 2D Wavelet transform-based and the proposed 3D Walsh transform-based sparsity bases were investigated in the l1-SPIRiT method. The experimental results show that the proposed 3D Walsh transform-based l1-SPIRiT method outperformed the original l1-SPIRiT in terms of image quality and computational efficiency

Перспективы развития рынка еврокапитала в современных условиях

Clinical and research staff who work around magnetic resonance imaging (MRI) scanners are exposed to the static magnetic stray fields of these scanners. Although the past decade has seen strong developments in the assessment of occupational exposure to electromagnetic fields from MRI scanners, there is insufficient insight into the exposure variability that characterizes routine MRI work practice. However, this is an essential component of risk assessment and epidemiological studies. This paper describes the results of a measurement survey of shift-based personal exposure to static magnetic fields (SMF) (B) and motion-induced time-varying magnetic fields (dB/dt) among workers at 15 MRI facilities in the Netherlands. With the use of portable magnetic field dosimeters, >400 full-shift and partial shift exposure measurements were collected among various jobs involved in clinical and research MRI. Various full-shift exposure metrics for B and motion-induced dB/dt exposure were calculated from the measurements, including instantaneous peak exposure and time-weighted average (TWA) exposures. We found strong correlations between levels of static (B) and time-varying (dB/dt) exposure (r = 0.88–0.92) and between different metrics (i.e. peak exposure, TWA exposure) to express full-shift exposure (r = 0.69–0.78). On average, participants were exposed to MRI-related SMFs during only 3.7% of their work shift. Average and peak B and dB/dt exposure levels during the work inside the MRI scanner room were highest among technical staff, research staff, and radiographers. Average and peak B exposure levels were lowest among cleaners, while dB/dt levels were lowest among anaesthesiology staff. Although modest exposure variability between workplaces and occupations was observed, variation between individuals of the same occupation was substantial, especially among research staff. This relatively large variability between workers with the same job suggests that exposure classification based solely on job title may not be an optimal grouping strategy for epidemiological purposes

Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network

Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing method that quantifies tissue magnetic susceptibility distributions. However, QSM acquisitions are relatively slow, even with parallel imaging. Incoherent undersampling and compressed sensing reconstruction techniques have been used to accelerate traditional magnitude-based MRI acquisitions; however, most do not recover the full phase signal due to its non-convex nature. In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM acquisition. Magnitude, phase, and QSM results from DCRNet were compared with two iterative and one deep learning methods on retrospectively undersampled acquisitions from six healthy volunteers, one intracranial hemorrhage and one multiple sclerosis patients, as well as one prospectively undersampled healthy subject using a 7T scanner. Peak signal to noise ratio (PSNR), structural similarity (SSIM) and region-of-interest susceptibility measurements are reported for numerical comparisons. The proposed DCRNet method substantially reduced artifacts and blurring compared to the other methods and resulted in the highest PSNR and SSIM on the magnitude, phase, local field, and susceptibility maps. It led to 4.0% to 8.8% accuracy improvements in deep grey matter susceptibility than some existing methods, when the acquisition was accelerated four times. The proposed DCRNet also dramatically shortened the reconstruction time by nearly 10 thousand times for each scan, from around 80 hours using conventional approaches to only 30 seconds.Comment: 10 figure

Validity and reliability of computerized measurement of lumbar intervertebral disc height and volume from magnetic resonance images

BACKGROUND CONTEXT: Magnetic resonance (MR) examinations of morphologic characteristics of intervertebral discs (IVDs) have been used extensively for biomechanical studies and clinical investigations of the lumbar spine. Traditionally, the morphologic measurements have been performed using time- and expertise-intensive manual segmentation techniques not well suited for analyses of large-scale studies.