Spatial resolution enhancement in photon-starved STED imaging using deep learning-based fluorescence lifetime analysis
Spatial resolution enhancement in photon-starved STED imaging using deep learning-based fluorescence lifetime analysis
复制标题
使用基于深度学习的荧光寿命分析增强光子匮乏 STED 成像的空间分辨率
DOI：
10.1039/d3nr00305a
复制
发表时间：
2023
期刊：
影响因子：
6.7
通讯作者：
Yeh, Hsin-Chih
中科院分区：
文献类型：
--
作者：
Chen, Yuan-I;Chang, Yin-Jui;Sun, Yuansheng;Liao, Shih-Chu;Santacruz, Samantha R.;Yeh, Hsin-Chih
作为一种超分辨率成像方法，受激发射耗尽（STED）显微镜已经揭开了细胞内的精细结构，并提供了对细胞内纳米级组织的见解。虽然图像分辨率可以通过不断增加STED光束功率来进一步提高，但由此产生的光损伤和光毒性是STED显微镜在现实世界中应用的主要问题。在这里，我们证明，在STED光束功率减少50%的情况下，STED图像分辨率可以通过使用寿命调谐（SPLIT）方案结合基于深度学习的相量分析算法（称为flimGANE）（基于生成对抗网络的荧光寿命成像）来分离光子提高高达1.45倍。这项工作提供了一种新的方法STED成像的情况下，只有有限的光子预算是可用的。
As a super-resolution imaging method, stimulated emission depletion (STED) microscopy has unraveled fine intracellular structures and provided insights into nanoscale organizations in cells. Although image resolution can be further enhanced by continuously increasing the STED-beam power, the resulting photodamage and phototoxicity are major issues for real-world applications of STED microscopy. Here we demonstrate that, with 50% less STED-beam power, the STED image resolution can be improved up to 1.45-fold using the separation of photons by a lifetime tuning (SPLIT) scheme combined with a deep learning-based phasor analysis algorithm termed flimGANE (fluorescence lifetime imaging based on a generative adversarial network). This work offers a new approach for STED imaging in situations where only a limited photon budget is available.