Deep learning-enabled high-performance multiphoton fluorescence vascular imaging using clinically approved fluorescent probes.

Journal: iScience
Published Date: Nov 19, 2025

Abstract

Among various modalities, multiphoton fluorescence imaging (MPFI) stands out for its exceptionally high spatial resolution in deep tissue imaging. Unfortunately, current clinically approved fluorescent probes are not engineered for MPFI, hindering the entry of MPFI into the clinical stage. Although several high-performance customized multiphoton probes have been developed, their biosafety has yet to be corroborated. To address this concern, we developed a deep learning-based method, trained on previously reported MPFI images enabled by aggregation-induced emissions luminogens nanoparticles, for high-performance MPFI using commercial q800 quantum dots and a clinically approved indocyanine green (ICG) probe. Remarkably, the proposed method demonstrated exceptional effectiveness when handling previously unseen data and showed strong optimization performance for MPFI images of cerebral microvasculature. Especially, blood vessels in the hippocampus region become clear and noise free after deep learning processing. Overall, this work offers a valuable strategy to greatly improve the practicality and applicability of MPFI.

Authors

  • Zhourui Xu
    Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518055, China.
  • Haoran Luo
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China.
  • Ting Chen
    CAS Key Laboratory of Tropical Marine Bio-resources and Ecology (LMB), Guangdong Provincial Key Laboratory of Applied Marine Biology (LAMB), South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China. [email protected].
  • Shoulong Yang
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China.
  • Junkang Peng
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China.
  • Yibin Zhang
    Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518055, China.
  • Yi Gao
    Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
  • Yonghong Shao
    Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
  • Wing-Cheung Law
    Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
  • Ken-Tye Yong
    School of Biomedical Engineering, The University of Sydney, Sydney, NSW, 2006, Australia.
  • Ke Wang
    China Electric Power Research Institute, Haidian District, Beijing 100192, China. [email protected].
  • Gaixia Xu
    Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518055, China. [email protected].

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