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 ima...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.展开更多
基金support by grants from the National Science and Technology Major Project Program(Grant Nos.2021YFA1100201,2022YFF0712500,and 2022YFC3400600)the National Natural Science Foundation of China(Grant Nos.92054301,81925022,92150301,32170691,62103071,and 31901061)+5 种基金the Beijing Natural Science Foundation(Grant No.Z20J00059)the Lingang Laboratory(Grant No.LG-QS-202206-06)Clinical Medicine Plus X-Young Scholars Project,Peking University,the Fundamental Research Funds for the Central Universities,the Natural Science Foundation of Chongqing(Grant No.cstc2021jcyj-msxmX0526)the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJQN202100630)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA16021200)the High-Performance Computing Platform of Peking University.
文摘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.
