Terahertz Metastructures for Noninvasive Biomedical Sensing and Characterization in Future Health Care [Bioelectromagnetics]

Nourinovin, Shohreh; Navarro‐Cía, Miguel; Rahman, Muhammad M; Philpott, Michael P.; Abbasi, Qammer H.; Alomainy, Akram

doi:10.1109/map.2022.3145712

Cited by 6 publications

(2 citation statements)

References 63 publications

(55 reference statements)

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“…Another image contrast parameters and cancer biomarkers those have been reported to contribute to THz image contrast include structural changes, cell-density, electrical interactions between agents and biological tissue as well as tissue substances like proteins, fiber and fat etc. [20], [21], [22], [23]. Also, the THz radiation is capable of evaluating the state of bacteria i.e., dead or live based on the changes in hydration levels [13].…”

Section: Introductionmentioning

confidence: 99%

Terahertz Imaging and Sensing for Healthcare: Current Status and Future Perspectives

Gezimati

Singh

2023

IEEE Access

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There is a keen interest in the exploration of new generation emitters and detectors due to advancements in innovation of new materials and device processing technologies which have opened up new frontiers in the Terahertz (THz) spectrum. Therefore, it is necessary to review the developments in THz technology for healthcare applications, their impact, implications and prospects for ongoing research and development. This paper provides a broad overview of the current status and prospects of application of THz imaging and sensing for the healthcare domain. We present current knowledge, identify existing challenges for wide scale clinical adoption of THz systems and prospective opinions to facilitate research and development towards optimized and miniaturized THz systems and biosensors that provide real operational convenience through emerging trends. Firstly, we provide an overview of the THz imaging and sensing techniques that exploit properties of THz generation and detection with emphasis on terahertz time domain spectroscopy (THz-TDS) and THz Metamaterials. The mechanisms of tissue image contrast and the application of THz imaging and sensing for biomedical applications in particular, the cancer detection application is reported. Secondly, an outlook toward the advancements in THz technology in the interface of healthcare 4.0 and its enabling technologies is explored for next generation smart and connected healthcare systems. Third, we identify the merits and existing challenges in THz cancer imaging and sensing and suggest prospective opinions to pave way to ongoing and future research. Further, we discuss the recent advances in THz imaging development and the contribution of near-field techniques based on plasmonic, and resonance based metasurfaces, waveguides etc. for breaking the diffraction limit towards development of THz systems that are convenient for point of care. We bring researchers a roadmap for future research scope.

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Section: Introductionmentioning

confidence: 99%

Terahertz Imaging and Sensing for Healthcare: Current Status and Future Perspectives

Gezimati

Singh

2023

IEEE Access

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“…The terahertz band is the region of the electromagnetic spectrum located between the microwave/millimeter wave and infrared frequency ranges. Usually defined between roughly 0.1 and 10 THz, [1] terahertz waves can be widely applied in non-destructive testing, [2,3] biomedical sensing, [4,5] imaging, [6,7] wireless communications, [8][9][10] etc. The rapid development of terahertz technologies, including active devices and passive electromagnetic components, is indispensable for the advancement and development of many of these applications.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Enables Multi‐Degree‐of‐Freedom Reconfigurable Terahertz Holograms with Cascaded Diffractive Optical Elements

Jia

Lin

Sensale‐Rodriguez

2023

Advanced Optical Materials

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In this context, terahertz holograms enabled by single-layer diffractive optical elements have been widely explored. [11,18,19,26] However, the reconfigurability of single-layer passive diffractive optical elements is very limited. Cascaded DOE structures have the advantage of enabling more functionality and information capacity compared to single-layer DOEs, as discussed in ref. [27]. Liao et al. [28] studied terahertz reconfigurable holographic imaging by mechanical translation of an absorber relative to two fixed discrete dielectric DOEs. Cascaded computer-generated phase-only diffractive optical elements for multiplane image formation were simulated by Alkan et al. [29] A lens was utilized in such a cascaded configuration for Fourier transform to project patterns on two imaging planes. In the visible range, Wang et al. [30] demonstrated azimuthal multiplexing with a stratified DOE layout optimized by iterative optimization algorithms. The optical information was encoded azimuthally in the DOEs and retrieved by rotating the DOEs relative to each other. It is to highlight that reconfigurable materials such as phase change materials, semiconductor layers, and MEMS structures integrated with metasurface structures [31][32][33][34][35][36] are also good candidates to realize reconfigurable terahertz devices and holograms.Diffractive optical neural networks (DONN) [12] have been successfully applied for the classification of handwritten digit images, [12,37] the design of spatially controlled wavelength demultiplexing systems, [38] and terahertz pulse shaping. [39] Such a machine learning method is an efficient and powerful tool for designing diffractive optics inverse problems in cascaded configurations. The pixel phase (height distributions) in the diffractive layers can be trained by stochastic gradient descent and error-backpropagation to achieve the desired diffraction pattern as the incident beam passes through the diffractive layers.The purpose of this work is to show that through such a DONN machine learning method, it is possible to realize holograms with a tailored reconfigurable operation when altering multiple degrees of freedom, i.e., either the number, spacing, rotational alignment, and/or order of the cascaded diffractive layers. The height distributions of the diffractive layers are trained by the network on the basis of an initial incident terahertz field distribution and predefined (target) diffraction patterns.For our proof-of-concept demonstration, five scenarios are demonstrated, each corresponding to the variation of an individual degree of freedom in the cascaded configuration. A Machine learning can empower the design of cascaded diffractive optical elements (DOEs) at terahertz frequencies enabling the realization of holograms with a tailored multi-degree-of-freedom reconfigurable operation when altering either the number, spacing, rotational alignment, and/or order of the elements. This unprecedented control over the spatial terahertz light distribution can profoundly impact multiple terahe...

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Enhanced 1-bit Programmable Metasurface Failure Diagnosis via Hadamard Matrices

Wu,

Wang

2024

2024 International Applied Computational Electromagnetics Society Symposium (ACES-China)

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Terahertz Metastructures for Noninvasive Biomedical Sensing and Characterization in Future Health Care [Bioelectromagnetics]

Cited by 6 publications

References 63 publications

Terahertz Imaging and Sensing for Healthcare: Current Status and Future Perspectives

Terahertz Imaging and Sensing for Healthcare: Current Status and Future Perspectives

Machine Learning Enables Multi‐Degree‐of‐Freedom Reconfigurable Terahertz Holograms with Cascaded Diffractive Optical Elements

Enhanced 1-bit Programmable Metasurface Failure Diagnosis via Hadamard Matrices

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