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  • 主办单位：
    中国光学工程学会清华大学上海理工大学
  • 名誉主编： 庄松林 院士
  • 国际主编： 顾敏 院士
  • 主       编：
    孙洪波 教授仇旻 教授
  • 创       刊：2020年3月
  • ISSN：2662-1991
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Ultra-low photodamage three-photon microscopy assisted by neural network for monitoring regenerative myogenesis
Yifei Li, Keying Li, Mubin He, Chenlin Liang, Wang Xi, Shuhong Qi, Runnan Zhang, Ming Jiang, Zheng Zheng, Zichen Wei, Xin Xie, Jun Qian
 doi: 10.1186/s43074-025-00191-6
Abstract(0) PDF(0)
Abstract:
Three-photon microscopy (3PM) enables high-resolution three-dimensional (3D) imaging in deeply situated and highly scattering biological specimens, facilitating precise characterization of biological morphology and cellular-level physiology in vivo. However, the use of fluorescent probes with relatively low three-photon absorption cross-sections necessitates high-peak-power lasers for excitation, which poses inherent risks of light-induced damage. Additionally, the low repetition frequency of these lasers prolongs scanning time per pixel, hampering imaging speed and exacerbating the potential for photodamage. Such limitations hinder the application of 3PM in studying vulnerable tissues, including muscle regeneration. To address this critical issue, we developed the Multi-Scale Attention Denoising Network (MSAD-Net), a precise and versatile denoising network suitable for diverse structures and varying noise levels. Our network enables the use of lower excitation power (1/4–1/2 of the common power: 1.0–1.5 mW vs 4–6 mW) and shorter scanning time (1/6–1/4 of the common time: 2–3 μs/pixel vs 12 μs/pixel) in 3PM while preserving image quality and tissue integrity. It achieves a structural similarity index (SSIM) of with an average of 0.9932 and a fast inference time of just 80 ms per frame which ensured both high fidelity and practicality for downstream applications. By utilizing MSAD-Net-assisted imaging, we characterize the biological morphology and functionality of muscle regeneration processes through deep in vivo five-channel imaging under low excitation power and short scanning time, while maintaining a high signal-to-noise ratio (SNR) and excellent axial spatial resolution. Furthermore, we conducted high axial-resolution dynamic imaging of vascular microcirculation, macrophages, and ghost fibers. Our findings provide a deeper understanding of the mechanisms underlying muscle regeneration at the cellular and tissue levels.
Machine-learning-powered efficient design of photonic crystal cavities
Li Liu, Yangcan Long
 doi: 10.1186/s43074-025-00201-7
Abstract(10) PDF(1)
Abstract:
While machine learning holds remarkable potential for designing high-quality (Q) photonic crystal (PC) cavities, its effectiveness heavily relies on the availability of thousands of data samples. This requirement necessitates substantial simulation resources and considerable time. To tackle the challenge of data scarcity in high-Q microcavity designs, we propose an innovative intelligent model for efficient data augmentation that entails merely a few hundred original samples. Notably, our novel structural reshaping strategy, involving the groundbreaking Euler-bend air-hole structure, significantly enhances the fabrication robustness, addressing the consistency difficulty associated with large-scale manufacturing of high-Q PC microcavity arrays. Silicon PC nanobeam cavities are experimentally demonstrated, featuring record-breaking loaded Q factors, large tolerance for the Euler-bend holes and extremely compact sizes of 6 μm2. Importantly, to emphasize the on-chip high-resolution signal processing, the cavity-based microwave photonic filters (MPFs) offer unprecedented capabilities, including ultra-narrow bandwidths, an unlimited frequency tuning range and ultra-high rejection ratios using a micrometer-scale cavity. This breakthrough truly transcends the traditional limitations between the filter size, frequency resolution and tuning range. These exceptional characteristics position our MPFs with a cavity-based record-breaking QMPF/S ratio (S: device size).
Frequency comb-based time-domain tracking of AFM cantilever dynamics from picometre-scale noise to micron-scale nonlinear motion
Yongjin Na, Junho Suh, Jungwon Kim
 doi: 10.1186/s43074-025-00197-0
Abstract(8) PDF(0)
Abstract:
The field of micro- and nano-mechanics has seen rapid advances driven by applications in sensing, microscopy, and precision instrumentation. Accurate, time-resolved characterization of mechanical dynamics is essential for understanding device behaviour and improving performance. However, conventional optical and electrical methods face trade-offs between sensitivity, linearity, and bandwidth, while frequency-domain approaches are limited in capturing transient dynamics. Here, we present a frequency comb-based time-domain tracking technique for directly observing the full-range dynamic motion of atomic force microscopy (AFM) micro-cantilevers. By leveraging electro-optic sampling between femtosecond optical pulses and ultra-precise photocurrent timing signals, our system enables real-time measurements across six orders of magnitude – from ~ 30 pm thermal fluctuations to ~ 20 µm nonlinear oscillations. The technique reveals complex behaviours including mode coupling, hysteresis, bifurcation, and transient modulation, while maintaining calibration fidelity through thermomechanical noise. This approach bridges the longstanding gap between ultra-sensitive and wide-range motion tracking, offering a powerful tool for studying nonlinear dynamics in micro- and nano-scale mechanical systems. Looking ahead, the method lays the groundwork for advances in high-resolution force sensing, AFM probe optimization, and the broader exploration of dynamic behaviour in precision microsystems.
Fiber laser based stimulated Raman photothermal microscopy towards a high-performance and user-friendly chemical imaging platform
Xiaowei Ge, Yifan Zhu, Dingcheng Sun, Hongli Ni, Yueming Li, Chinmayee V. Prabhu Dessai, Ji-Xin Cheng
 doi: 10.1186/s43074-025-00196-1
Abstract(14) PDF(1)
Abstract:
Stimulated Raman scattering (SRS) microscopy is a highly sensitive chemical imaging technique. However, the SRS imaging performance hinges on two key factors: the reliance on low-noise but bulky solid-state laser sources and stringent sample requirements necessitated by high numerical aperture (NA) optics. Here, we present a fiber laser based stimulated Raman photothermal (SRP) microscope that addresses these limitations. While appreciating the portability and compactness of a noisy source, fiber laser SRP enables a two-order-of-magnitude improvement in signal to noise ratio over fiber laser SRS without balance detection. Furthermore, with the use of low NA, long working distance optics for signal collection, SRP expands the allowed sample space from millimeters to centimeters, which diversifies the sample formats to multi-well plates and thick tissues. The sensitivity and imaging depth are further amplified by using urea for both thermal enhancement and tissue clearance. Together, fiber laser SRP microscopy provides a robust, user-friendly platform for diverse applications.
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