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

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Yifei Li, Keying Li, Mubin He, Chenlin Liang, Wang Xi, Shuhong Qi, Runnan Zhang, Ming Jiang, Zheng Zheng, Zichen Wei, Xin Xie, Jun Qian. Ultra-low photodamage three-photon microscopy assisted by neural network for monitoring regenerative myogenesis[J]. PhotoniX. doi: 10.1186/s43074-025-00191-6

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Ultra-low photodamage three-photon microscopy assisted by neural network for monitoring regenerative myogenesis

doi: 10.1186/s43074-025-00191-6

1.
State Key Laboratory of Extreme Photonics and Instrumentation, Centre for Optical and Electromagnetic Research, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, 310058, China
2.
Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Haining, 314400, China
3.
Tandon School of Engineering, New York University, New York, 11201, USA
4.
The Second Affiliated Hospital, School of Medicine, Interdisciplinary Institute of Neuroscience and Technology (ZIINT), Zhejiang University, Hangzhou, 310020, China
5.
Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
6.
The School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
7.
Center for Genetic Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
8.
School of Chemistry and Chemical Engineering, Hefei University of Technology, Hefei, 230009, China

Funds: The authors acknowledge Live Multiphoton Microscopy of Zhejiang University 7T Magnetic Resonance Imaging Platform and Fen Yang for technical assistance, Shuangshuang Liu from the Core Facilities, Zhejiang University School of Medicine for her technical support, and Prof. Dongyu Li from Huazhong University of Science and Technology, for his constructive suggestion.

Received Date: 2025-05-22
Accepted Date: 2025-08-18
Rev Recd Date: 2025-07-21

Available Online: 2025-10-09

Abstract

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.
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References(40)

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