The integration of surface coils and metamaterials presents a promising approach for enhancing the performance of Magnetic Resonance Imaging (MRI) systems. This combination offers substantial benefits in terms of local staging flexibility, allowing for improved imaging capabilities in specific region of interest. However, due to the coupling between the two, low signal-to-noise ratio (SNR) and low contrast resolution further prevent the future development of clinical MRI. In this paper, we propose a conformal metasurface decoupling isolator for magnetic field enhancement in 1.5T surface coil MRI systems. We use conformal optical theory and polarization conversion mechanisms to eliminate the coupling between metamaterial and radio frequency surface coil. In addition, the conformal theory can be applied to the analysis of any surface boundary condition, providing an effective method for the design of magnetic resonance metamaterials. Through conformal optical analysis, portable metasurface designs can be achieved, opening up a promising avenue for easy-to-use surface-coil MRI scanners.
KEYWORDS: Magnetism, Electromagnetic metamaterials, Electromagnetism, Magnetic resonance imaging, Neural networks, Design, Deep learning, Education and training, Data modeling, Image processing
Metamaterials, due to their extraordinary physical properties not found in natural materials, can control their electromagnetic properties by adjusting their geometry and structural parameters. Traditional MRl systems are often affected by magnetic field distortion and signal attenuation caused by metal structures. The introduction of metamaterials provides a new way to overcome these problems. However, designing metamaterials in the traditional way can present highly complex optimization challenges. In order to solve this problem, we introduce deep learning technology to study the relationship between the properties of metamaterials and the electromagnetic response under electromagnetic drive by training neural networks, and the experimental results show that compared with traditional hand-designed metamaterials. Metamaterials optimized by deep learning show superior performance in MRl systems. The combination of deep learning and electromagnetic metamaterial design opens up new directions for the development of MRI technology and has the potential to advance the entire field of healthcare.
Image dehazing has been widely used in vision-based fields, such as detection, segmentation, traffic monitoring, and automated vehicle system. However, most of the existing end-to-end dehazing networks are fully data driven without physical constraints or prior information guidance, leading to difficulties in exploring latent structures and statistical characteristics of hazy images. We propose a novel Retinex decomposition-fusion dehazing network consisting of a dual-branch decomposition module and a fusion optimization module. Different from existing solutions, we decompose the clear images in the commonly used RESIDE dataset based on Retinex theory to construct the clear illumination map and reflection map datasets to drive the network training, equivalently imposing reasonable constraints on the network and achieving impressive dehazing performances. The dual-branch decomposition module is developed to estimate the illumination map and the reflection map, respectively. The illumination map mainly contains the global features of the image, whereas the reflection map reflects the inherent color properties of the image and contains rich details, with which we explore the latent structures and statistical characteristics of hazy images. In addition, the dual-branch structure avoids the error accumulation and information cancellation existing in current methods. Subsequently, the estimated illumination map and reflection map are fused and refined via the fusion optimization module to access the dehazed image. Experiments show that the proposed network has better generalization and visual effects than existing fully data-driven methods and can be applied successfully to real-world scenarios.
Most existing dehazing approaches employ image priors such as a dark channel prior, which may be violated in the nighttime. In addition, these approaches perform dehazing directly on the original hazy image, which usually results in detail blurring and noise amplifying. To address both issues, an effective single nighttime image haze removal approach is presented. We first decompose the hazy image into a structure layer and a texture layer. Specifically, the dehazing operation is performed only on the structure layer to avoid detail loss and noise amplification. Due to the low frequency characteristics of the haze and the glow of artificial lights, the atmospheric veil is estimated on structure layer dehazing. Furthermore, to eliminate the nonuniform illumination effects, we present an ambient light estimation approach based on multiscale fusion. At the same time, the denoising is performed in the texture layer. Extensive experiments demonstrate that the proposed approach recovers a high-quality, haze-free, and noise-free image with vivid details.
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