Reviews|7 Article(s)
Reviews
Recent advances in deep-learning-enhanced photoacoustic imaging
Jinge Yang, Seongwook Choi, Jiwoong Kim, Byullee Park, and Chulhong Kim
Photoacoustic imaging (PAI), recognized as a promising biomedical imaging modality for preclinical and clinical studies, uniquely combines the advantages of optical and ultrasound imaging. Despite PAI’s great potential to provide valuable biological information, its wide application has been hindered by technical limitations, such as hardware restrictions or lack of the biometric information required for image reconstruction. We first analyze the limitations of PAI and categorize them by seven key challenges: limited detection, low-dosage light delivery, inaccurate quantification, limited numerical reconstruction, tissue heterogeneity, imperfect image segmentation/classification, and others. Then, because deep learning (DL) has increasingly demonstrated its ability to overcome the physical limitations of imaging modalities, we review DL studies from the past five years that address each of the seven challenges in PAI. Finally, we discuss the promise of future research directions in DL-enhanced PAI. Photoacoustic imaging (PAI), recognized as a promising biomedical imaging modality for preclinical and clinical studies, uniquely combines the advantages of optical and ultrasound imaging. Despite PAI’s great potential to provide valuable biological information, its wide application has been hindered by technical limitations, such as hardware restrictions or lack of the biometric information required for image reconstruction. We first analyze the limitations of PAI and categorize them by seven key challenges: limited detection, low-dosage light delivery, inaccurate quantification, limited numerical reconstruction, tissue heterogeneity, imperfect image segmentation/classification, and others. Then, because deep learning (DL) has increasingly demonstrated its ability to overcome the physical limitations of imaging modalities, we review DL studies from the past five years that address each of the seven challenges in PAI. Finally, we discuss the promise of future research directions in DL-enhanced PAI.
Advanced Photonics Nexus
- Publication Date: Jul. 24, 2023
- Vol. 2, Issue 5, 054001 (2023)
Review on near-field detection technology in the biomedical field
Xitian Hu, Li Zhou, Xu Wu, and Yan Peng
We review the recent biomedical detection developments of scanning near-field optical microscopy (SNOM), focusing on scattering-type SNOM, atomic force microscope-based infrared spectroscopy, peak force infrared microscopy, and photo-induced force microscopy, which have the advantages of label-free, noninvasive, and specific spectral recognition. Considering the high water content of biological samples and the strong absorption of water by infrared waves, we divide the relevant research on these techniques into two categories: one based on a nonliquid environment and the other based on a liquid environment. In the nonliquid environment, the chemical composition and structural information of biomedical samples can be obtained with nanometer resolution. In the liquid environment, these techniques can be used to monitor the dynamic chemical reaction process and track the process of chemical composition and structural change of single molecules, which is conducive to exploring the development mechanism of physiological processes. We elaborate their experimental challenges, technical means, and actual cases for three microbiomedical samples (including biomacromolecules, cells, and tissues). We also discuss the prospects and challenges for their development. Our work lays a foundation for the rational design and efficient use of near-field optical microscopy to explore the characteristics of microscopic biology. We review the recent biomedical detection developments of scanning near-field optical microscopy (SNOM), focusing on scattering-type SNOM, atomic force microscope-based infrared spectroscopy, peak force infrared microscopy, and photo-induced force microscopy, which have the advantages of label-free, noninvasive, and specific spectral recognition. Considering the high water content of biological samples and the strong absorption of water by infrared waves, we divide the relevant research on these techniques into two categories: one based on a nonliquid environment and the other based on a liquid environment. In the nonliquid environment, the chemical composition and structural information of biomedical samples can be obtained with nanometer resolution. In the liquid environment, these techniques can be used to monitor the dynamic chemical reaction process and track the process of chemical composition and structural change of single molecules, which is conducive to exploring the development mechanism of physiological processes. We elaborate their experimental challenges, technical means, and actual cases for three microbiomedical samples (including biomacromolecules, cells, and tissues). We also discuss the prospects and challenges for their development. Our work lays a foundation for the rational design and efficient use of near-field optical microscopy to explore the characteristics of microscopic biology.
Advanced Photonics Nexus
- Publication Date: Jul. 17, 2023
- Vol. 2, Issue 4, 044002 (2023)
Printable organic light-emitting diodes for next-generation visible light communications: a review
Kunping Guo, Zhe Tang, Xingxing Chou, Saihu Pan, Chunchen Wan, Tao Xue, Liping Ding, Xiao Wang, Jin Huang, Fanghui Zhang, and Bin Wei
Visible light communication (VLC) is an emerging technology employing light-emitting diodes (LEDs) to provide illumination and wireless data transmission simultaneously. Harnessing cost-efficient printable organic LEDs (OLEDs) as environmentally friendly transmitters in VLC systems is extremely attractive for future applications in spectroscopy, the internet of things, sensing, and optical ranging in general. Here, we summarize the latest research progress on emerging semiconductor materials for LED sources in VLC, and highlight that OLEDs based on nontoxic and cost-efficient organic semiconductors have great opportunities for optical communication. We further examine efforts to achieve high-performance white OLEDs for general lighting, and, in particular, focus on the research status and opportunities for OLED-based VLC. Different solution-processable fabrication and printing strategies to develop high-performance OLEDs are also discussed. Finally, an outlook on future challenges and potential prospects of the next-generation organic VLC is provided. Visible light communication (VLC) is an emerging technology employing light-emitting diodes (LEDs) to provide illumination and wireless data transmission simultaneously. Harnessing cost-efficient printable organic LEDs (OLEDs) as environmentally friendly transmitters in VLC systems is extremely attractive for future applications in spectroscopy, the internet of things, sensing, and optical ranging in general. Here, we summarize the latest research progress on emerging semiconductor materials for LED sources in VLC, and highlight that OLEDs based on nontoxic and cost-efficient organic semiconductors have great opportunities for optical communication. We further examine efforts to achieve high-performance white OLEDs for general lighting, and, in particular, focus on the research status and opportunities for OLED-based VLC. Different solution-processable fabrication and printing strategies to develop high-performance OLEDs are also discussed. Finally, an outlook on future challenges and potential prospects of the next-generation organic VLC is provided.
Advanced Photonics Nexus
- Publication Date: May. 13, 2023
- Vol. 2, Issue 4, 044001 (2023)
Supporting quantum technologies with an ultralow-loss silicon photonics platform
Matteo Cherchi, Arijit Bera, Antti Kemppinen, Jaani Nissilä, Kirsi Tappura, Marco Caputo, Lauri Lehtimäki, Janne Lehtinen, Joonas Govenius, Tomi Hassinen, Mika Prunnila, and Timo Aalto
Photonic integrated circuits (PICs) are expected to play a significant role in the ongoing second quantum revolution, thanks to their stability and scalability. Still, major upgrades are needed for available PIC platforms to meet the demanding requirements of quantum devices. We present a review of our recent progress in upgrading an unconventional silicon photonics platform toward this goal, including ultralow propagation losses, low-fiber coupling losses, integration of superconducting elements, Faraday rotators, fast and efficient detectors, and phase modulators with low-loss and/or low-energy consumption. We show the relevance of our developments and our vision in the main applications of quantum key distribution, to achieve significantly higher key rates and large-scale deployment; and cryogenic quantum computers, to replace electrical connections to the cryostat with optical fibers. Photonic integrated circuits (PICs) are expected to play a significant role in the ongoing second quantum revolution, thanks to their stability and scalability. Still, major upgrades are needed for available PIC platforms to meet the demanding requirements of quantum devices. We present a review of our recent progress in upgrading an unconventional silicon photonics platform toward this goal, including ultralow propagation losses, low-fiber coupling losses, integration of superconducting elements, Faraday rotators, fast and efficient detectors, and phase modulators with low-loss and/or low-energy consumption. We show the relevance of our developments and our vision in the main applications of quantum key distribution, to achieve significantly higher key rates and large-scale deployment; and cryogenic quantum computers, to replace electrical connections to the cryostat with optical fibers.
Advanced Photonics Nexus
- Publication Date: Apr. 06, 2023
- Vol. 2, Issue 2, 024002 (2023)
Evolution on spatial patterns of structured laser beams: from spontaneous organization to multiple transformations
Xin Wang, Zilong Zhang, Xing Fu, Adnan Khan, Suyi Zhao, Yuan Gao, Yuchen Jie, Wei He, Xiaotian Li, Qiang Liu, and Changming Zhao
Spatial patterns are a significant characteristic of lasers. The knowledge of spatial patterns of structured laser beams is rapidly expanding, along with the progress of studies on laser physics and technology. Particularly in the last decades, owing to the in-depth attention on structured light with multiple degrees of freedom, the research on spatial and spatiotemporal structures of laser beams has been promptly developed. Such beams have hatched various breakthroughs in many fields, including imaging, microscopy, metrology, communication, optical trapping, and quantum information processing. Here, we would like to provide an overview of the extensive research on several areas relevant to spatial patterns of structured laser beams, from spontaneous organization to multiple transformations. These include the early theory of beam pattern formation based on the Maxwell–Bloch equations, the recent eigenmodes superposition theory based on the time-averaged Helmholtz equations, the beam patterns extension of ultrafast lasers to the spatiotemporal beam structures, and the structural transformations in the nonlinear frequency conversion process of structured beams. Spatial patterns are a significant characteristic of lasers. The knowledge of spatial patterns of structured laser beams is rapidly expanding, along with the progress of studies on laser physics and technology. Particularly in the last decades, owing to the in-depth attention on structured light with multiple degrees of freedom, the research on spatial and spatiotemporal structures of laser beams has been promptly developed. Such beams have hatched various breakthroughs in many fields, including imaging, microscopy, metrology, communication, optical trapping, and quantum information processing. Here, we would like to provide an overview of the extensive research on several areas relevant to spatial patterns of structured laser beams, from spontaneous organization to multiple transformations. These include the early theory of beam pattern formation based on the Maxwell–Bloch equations, the recent eigenmodes superposition theory based on the time-averaged Helmholtz equations, the beam patterns extension of ultrafast lasers to the spatiotemporal beam structures, and the structural transformations in the nonlinear frequency conversion process of structured beams.
Advanced Photonics Nexus
- Publication Date: Feb. 06, 2023
- Vol. 2, Issue 2, 024001 (2023)
Recent advances in photonics of three-dimensional Dirac semimetal Cd3As2
Renlong Zhou, Kaleem Ullah, Naveed Hussain, Mohammed M. Fadhali, Sa Yang, Qiawu Lin, Muhammad Zubair, and Muhammad Faisal Iqbal
Due to their unusual features in condensed matter physics and their applicability in optical and optoelectronic applications, three-dimensional Dirac semimetals (3D DSMs) have garnered substantial interest in recent years. In contrast to monolayer graphene, 3D DSM exhibits linear band dispersion despite its macroscopic thickness. Therefore, being a bulk material, it is easy to make nanostructures with 3D DSM, just as one normally does with metals such as gold and silver. Among 3D DSMs, cadmium arsenide (Cd3As2) is quite famous and considered an excellent 3D DSM due to its chemical stability in air and extraordinary optical response. In this review, advances in 3D DSM Cd3As2 fabrication techniques and recent progress in the photonics of 3D DSM Cd3As2 are given and briefly reviewed. Various photonic features, including linear and nonlinear plasmonics, optical absorption, optical harmonic generation, and ultrafast dynamics, have been explored in detail. It is expected that Cd3As2 would share an excellent tunable photonic response like graphene. We envision that this article may serve as a concise overview of the recent progress of photonics in 3D DSM Cd3As2 and provides a compact reference for young researchers. Due to their unusual features in condensed matter physics and their applicability in optical and optoelectronic applications, three-dimensional Dirac semimetals (3D DSMs) have garnered substantial interest in recent years. In contrast to monolayer graphene, 3D DSM exhibits linear band dispersion despite its macroscopic thickness. Therefore, being a bulk material, it is easy to make nanostructures with 3D DSM, just as one normally does with metals such as gold and silver. Among 3D DSMs, cadmium arsenide (Cd3As2) is quite famous and considered an excellent 3D DSM due to its chemical stability in air and extraordinary optical response. In this review, advances in 3D DSM Cd3As2 fabrication techniques and recent progress in the photonics of 3D DSM Cd3As2 are given and briefly reviewed. Various photonic features, including linear and nonlinear plasmonics, optical absorption, optical harmonic generation, and ultrafast dynamics, have been explored in detail. It is expected that Cd3As2 would share an excellent tunable photonic response like graphene. We envision that this article may serve as a concise overview of the recent progress of photonics in 3D DSM Cd3As2 and provides a compact reference for young researchers.
Advanced Photonics Nexus
- Publication Date: Nov. 14, 2022
- Vol. 1, Issue 2, 024001 (2022)
Deep learning spatial phase unwrapping: a comparative review|Article Video
Kaiqiang Wang, Qian Kemao, Jianglei Di, and Jianlin Zhao
Phase unwrapping is an indispensable step for many optical imaging and metrology techniques. The rapid development of deep learning has brought ideas to phase unwrapping. In the past four years, various phase dataset generation methods and deep-learning-involved spatial phase unwrapping methods have emerged quickly. However, these methods were proposed and analyzed individually, using different strategies, neural networks, and datasets, and applied to different scenarios. It is thus necessary to do a detailed comparison of these deep-learning-involved methods and the traditional methods in the same context. We first divide the phase dataset generation methods into random matrix enlargement, Gauss matrix superposition, and Zernike polynomials superposition, and then divide the deep-learning-involved phase unwrapping methods into deep-learning-performed regression, deep-learning-performed wrap count, and deep-learning-assisted denoising. For the phase dataset generation methods, the richness of the datasets and the generalization capabilities of the trained networks are compared in detail. In addition, the deep-learning-involved methods are analyzed and compared with the traditional methods in ideal, noisy, discontinuous, and aliasing cases. Finally, we give suggestions on the best methods for different situations and propose the potential development direction for the dataset generation method, neural network structure, generalization ability enhancement, and neural network training strategy for the deep-learning-involved spatial phase unwrapping methods. Phase unwrapping is an indispensable step for many optical imaging and metrology techniques. The rapid development of deep learning has brought ideas to phase unwrapping. In the past four years, various phase dataset generation methods and deep-learning-involved spatial phase unwrapping methods have emerged quickly. However, these methods were proposed and analyzed individually, using different strategies, neural networks, and datasets, and applied to different scenarios. It is thus necessary to do a detailed comparison of these deep-learning-involved methods and the traditional methods in the same context. We first divide the phase dataset generation methods into random matrix enlargement, Gauss matrix superposition, and Zernike polynomials superposition, and then divide the deep-learning-involved phase unwrapping methods into deep-learning-performed regression, deep-learning-performed wrap count, and deep-learning-assisted denoising. For the phase dataset generation methods, the richness of the datasets and the generalization capabilities of the trained networks are compared in detail. In addition, the deep-learning-involved methods are analyzed and compared with the traditional methods in ideal, noisy, discontinuous, and aliasing cases. Finally, we give suggestions on the best methods for different situations and propose the potential development direction for the dataset generation method, neural network structure, generalization ability enhancement, and neural network training strategy for the deep-learning-involved spatial phase unwrapping methods.
Advanced Photonics Nexus
- Publication Date: Aug. 03, 2022
- Vol. 1, Issue 1, 014001 (2022)
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