Time-resolved volumetric fluorescence imaging over an extended duration with high spatial/temporal resolution is a key driving force in biomedical research for investigating spatial-temporal dynamics at organism-level...Time-resolved volumetric fluorescence imaging over an extended duration with high spatial/temporal resolution is a key driving force in biomedical research for investigating spatial-temporal dynamics at organism-level systems,yet it remains a major challenge due to the trade-off among imaging speed,light exposure,illumination power,and image quality.Here,we present a deep-learning enhanced light sheet fluorescence microscopy(LSFM)approach that addresses the restoration of rapid volumetric time-lapse imaging with less than 0.03%light exposure and 3.3%acquisition time compared to a typical standard acquisition.We demonstrate that the convolutional neural network(CNN)-transformer network developed here,namely U-net integrated transformer(UI-Trans),successfully achieves the mitigation of complex noise-scattering-coupled degradation and outperforms state-of-the-art deep learning networks,due to its capability of faithfully learning fine details while comprehending complex global features.With the fast generation of appropriate training data via flexible switching between confocal line-scanning LSFM(LS-LSFM)and conventional LSFM,this method achieves a three-to five-fold signal-to-noise ratio(SNR)improvement and~1.8 times contrast improvement in ex vivo zebrafish heart imaging and long-term in vivo 4D(3D morphology+time)imaging of heartbeat dynamics at different developmental stages with ultra-economical acquisitions in terms of light dosage and acquisition time.展开更多
基金supported by National Natural Science Foundation of China(52122008,52270008,52370003,62025502)Guangdong Introducing Innovative and Entrepreneurial Teams of“The Pearl River Talent Recruitment Program”(2021ZT09X044)Shenzhen Technology University under Grant JSZZ202301010.
文摘Time-resolved volumetric fluorescence imaging over an extended duration with high spatial/temporal resolution is a key driving force in biomedical research for investigating spatial-temporal dynamics at organism-level systems,yet it remains a major challenge due to the trade-off among imaging speed,light exposure,illumination power,and image quality.Here,we present a deep-learning enhanced light sheet fluorescence microscopy(LSFM)approach that addresses the restoration of rapid volumetric time-lapse imaging with less than 0.03%light exposure and 3.3%acquisition time compared to a typical standard acquisition.We demonstrate that the convolutional neural network(CNN)-transformer network developed here,namely U-net integrated transformer(UI-Trans),successfully achieves the mitigation of complex noise-scattering-coupled degradation and outperforms state-of-the-art deep learning networks,due to its capability of faithfully learning fine details while comprehending complex global features.With the fast generation of appropriate training data via flexible switching between confocal line-scanning LSFM(LS-LSFM)and conventional LSFM,this method achieves a three-to five-fold signal-to-noise ratio(SNR)improvement and~1.8 times contrast improvement in ex vivo zebrafish heart imaging and long-term in vivo 4D(3D morphology+time)imaging of heartbeat dynamics at different developmental stages with ultra-economical acquisitions in terms of light dosage and acquisition time.
