北京航空航天大学Chen Lingling课题组取得一项新突破。他们发现深度学习增强光片荧光显微镜对斑马鱼心脏跳动的体内4D成像。相关论文于2025年2月25日发表在《光：科学与应用》杂志上。

研究人员表示，长时间高时空分辨率的时间分辨体积荧光成像，是生物医学中研究生物体系统时空动态的关键驱动力，但由于成像速度、光照、照明功率和图像质量之间的权衡，它仍然是一个主要挑战。

研究人员提出了一种深度学习增强光片荧光显微镜（LSFM）方法，与典型的标准采集相比，该方法可以在小于0.03%的光照和3.3%的采集时间下，恢复快速体积延时成像。

研究人员证明了这里开发的卷积神经网络(CNN)-变压器网络，即U-net集成变压器（UI-Trans），成功地实现了复杂噪声散射耦合退化的缓解，并且优于最先进的深度学习网络，因为它能够忠实地学习精细细节，同时理解复杂的全局特征。

通过在共聚焦线扫描LSFM （LS-LSFM）和常规LSFM之间的灵活切换，快速生成合适的训练数据，该方法在离体斑马鱼心脏成像和不同发育阶段的长期体内4D （3D形态+时间）成像中，实现了3 - 5倍的信噪比（SNR）提高和约1.8倍的对比度提高，并且在光剂量和采集时间方面具有超经济的获取。

附：英文原文

Title: Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D imaging of zebrafish heart beating

Author: Zhang, Meng, Li, Renjian, Fu, Songnian, Kumar, Sunil, Mcginty, James, Qin, Yuwen, Chen, Lingling

Issue&Volume: 2025-02-25

Abstract: 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.

DOI: 10.1038/s41377-024-01710-z

Source: https://www.nature.com/articles/s41377-024-01710-z