1Peking University, Institute of Molecular Medicine, College of Future Technology, Center for Life Sciences, State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Beijing, China
2Peking University, School of Software and Microelectronics, Beijing, China
3Chongqing University of Posts and Telecommunications, College of Computer Science and Technology, Chongqing Key Laboratory of Image Cognition, Chongqing, China
4Peking University, Biomedical Engineering Department, Beijing, China
5Peking University, International Cancer Institute, Beijing, China
6PKU-IDG/McGovern Institute for Brain Research, Beijing, China
7Beijing Academy of Artificial Intelligence, Beijing, China
8National Biomedical Imaging Center, Beijing, China
Jianyong Wang, Junchao Fan, Bo Zhou, Xiaoshuai Huang, Liangyi Chen. Hybrid reconstruction of the physical model with the deep learning that improves structured illumination microscopy[J]. Advanced Photonics Nexus, 2023, 2(1): 016012
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Structured illumination microscopy (SIM) has been widely used in live-cell superresolution (SR) imaging. However, conventional physical model-based SIM SR reconstruction algorithms are prone to artifacts in handling raw images with low signal-to-noise ratios (SNRs). Deep-learning (DL)-based methods can address this challenge but may lead to degradation and hallucinations. By combining the physical inversion model with a total deep variation (TDV) regularization, we propose a hybrid restoration method (TDV-SIM) that outperforms conventional or DL methods in suppressing artifacts and hallucinations while maintaining resolutions. We demonstrate the performance superiority of TDV-SIM in restoring actin filaments, endoplasmic reticulum, and mitochondrial cristae from extremely low SNR raw images. Thus TDV-SIM represents the ideal method for prolonged live-cell SR imaging with minimal exposure and photodamage. Overall, TDV-SIM proves the power of integrating model-based reconstruction methods with DL ones, possibly leading to the rapid exploration of similar strategies in high-fidelity reconstructions of other microscopy methods.
Jianyong Wang, Junchao Fan, Bo Zhou, Xiaoshuai Huang, Liangyi Chen. Hybrid reconstruction of the physical model with the deep learning that improves structured illumination microscopy[J]. Advanced Photonics Nexus, 2023, 2(1): 016012