Enhancing signal-to-noise ratio of clinical 1.5T MRI using metasurface-inspired flexible wraps

Gupta, Jegyasu; Das, Priyanka; Bhattacharjee, Ratnajit; Sikdar, Debabrata

doi:10.1007/s00339-023-06962-x

Cited by 8 publications

(1 citation statement)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…THz metasurfaces, characterized by microscale periodic structures with distinctive electromagnetic properties, offer effective solutions, including high-resolution imaging and rapid detection [3][4][5][6][7]. In the realm of biomedical science, these metasurfaces hold vast potential for advancing diagnostic and therapeutic technologies [8,9].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enhanced Inverse Modeling of Terahertz Metasurface Based on a Convolutional Neural Network Technique

Gao,

Jiang,

Zhu

et al. 2024

Photonics

View full text Add to dashboard Cite

The traditional design method for terahertz metasurface biosensors is cumbersome and time-consuming, requires expertise, and often leads to significant discrepancies between expected and actual values. This paper presents a novel approach for the fast, efficient, and convenient inverse design of THz metasurface sensors, leveraging convolutional neural network techniques based on deep learning. During the model training process, the magnitude data of the scattering parameters collected from the numerical simulation of the THz metasurface served as features, paired with corresponding surface structure matrices as labels to form the training dataset. During the validation process, the thoroughly trained model precisely predicted the expected surface structure matrix of a THz metasurface. The results demonstrate that the proposed algorithm realizes time-saving, high-efficiency, and high-precision inversion methods without complicated data preprocessing and additional optimization algorithms. Therefore, deep learning algorithms offer a novel approach for swiftly designing and optimizing THz metasurface sensors in biomedical detection, bypassing the complex and specialized design process of electromagnetic devices, and promising extensive prospects for their application in the biomedical field.

show abstract