Effect of spatial spectrum overlap on Fourier ptychographic microscopy

Fourier ptychographic microscopy(FPM)is a newly developed imaging technique which stands out by virtue of its high-resolution and wide FOV.It improves a microscope's imaging perfor-mance beyond the diffraction limit of the employed optical components by illuminating the sample with oblique waves of different incident angles,similar to the concept of synthetic aperture.We propose to use an objective lens with high-NA to generate oblique illuminating waves in FPM.We demonstrate utilizing an objective lens with higher NA to iluminate the sample leads to better resolution by simulations,in which a resolution of 0.28 pum is achieved by using a high-NA illuminating objective lens(NA=1.49)and a low-NA collecting objective lens(NA=0.2)in coherent imaging(λ=488 nm).We then deeply study FPM's exact relevance of convergence speed to spatial spectrum overlap in frequency domain.The simulation results show that an overlap of about 60%is the optimal choice to acquire a high-quality recovery(520*520 pixels)with about 2 min's computing time.In addition,we testify the robustness of the algorithm of FPM to additive noises and its suitability for phase objects,which further proves FPM's potential application in biomedical imaging.

Adaptive optical quantitative phase imaging based on annular illumination Fourier ptychographic microscopy

Quantitative phase imaging(QPI)has emerged as a valuable tool for biomedical research thanks to its unique capabilities for quantifying optical thickness variation of living cells and tissues.Among many QPI methods,Fourier ptychographic microscopy(FPM)allows long-term label-free observation and quantitative analysis of large cell populations without compromising spatial and temporal resolution.However,high spatio-temporal resolution imaging over a long-time scale(from hours to days)remains a critical challenge:optically inhomogeneous structure of biological specimens as well as mechanical perturbations and thermal fluctuations of the microscope body all result in time-varying aberration and focus drifts,significantly degrading the imaging performance for long-term study.Moreover,the aberrations are sample-and environmentdependent,and cannot be compensated by a fixed optical design,thus necessitating rapid dynamic correction in the imaging process.Here,we report an adaptive optical QPI method based on annular illumination FPM.In this method,the annular matched illumination configuration(i.e.,the illumination numerical aperture(NA)strictly equals to the objective NA),which is the key for recovering low-frequency phase information,is further utilized for the accurate imaging aberration characterization.By using only 6 low-resolution images captured with 6 different illumination angles matching the NA of a 10x,0.4 NA objective,we recover high-resolution quantitative phase images(synthetic NA of 0.8)and characterize the aberrations in real time,restoring the optimum resolution of the system adaptively.Applying our method to live-cell imaging,we achieve diffraction-limited performance(full-pitch resolution of 655 nm at a wavelength of 525 nm)across a wide field of view(1.77mm2)over an extended period of time.

Hybrid deep-learning and physics-based neural network for programmable illumination computational microscopy

Two mainstream approaches for solving inverse sample reconstruction problems in programmable illumination computational microscopy rely on either deep models or physical models.Solutions based on physical models possess strong generalization capabilities while struggling with global optimization of inverse problems due to a lack of sufficient physical constraints.In contrast,deep-learning methods have strong problem-solving abilities,but their generalization ability is often questioned because of the unclear physical principles.In addition,conventional deep models are difficult to apply to some specific scenes because of the difficulty in acquiring high-quality training data and their limited capacity to generalize across different scenarios.To combine the advantages of deep models and physical models together,we propose a hybrid framework consisting of three subneural networks(two deep-learning networks and one physics-based network).We first obtain a result with rich semantic information through a light deeplearning neural network and then use it as the initial value of the physical network to make its output comply with physical process constraints.These two results are then used as the input of a fusion deeplearning neural work that utilizes the paired features between the reconstruction results of two different models to further enhance imaging quality.The proposed hybrid framework integrates the advantages of both deep models and physical models and can quickly solve the computational reconstruction inverse problem in programmable illumination computational microscopy and achieve better results.We verified the feasibility and effectiveness of the proposed hybrid framework with theoretical analysis and actual experiments on resolution targets and biological samples.

Dark-vacuole Bodies Studying by High-resolution Label-free Microscopy Assisted with Fluorescence Technology

The intracellular logistics system,consisting of vesicles,plays a crucial role in cellular transport.However,there is a lack of research on the types and functions of intracellular vesicles,and new technologies are needed for further investigation.Recently,researchers have discovered a new subcellular structure known as Dark-vacuole bodies.The composition,function,and potential synergy with other organelles of these Dark-vacuole bodies remain unclear.In this study,we utilized the high-resolution label-free Fourier ptychographic microscopy,developed by our research group,along with fluorescence confocal technology,to study and analyze Dark-vacuole bodies.Our findings provide evidence of the influence of Dark-vacuole bodies on the morphology,distribution,movement,and cell cycle of living cells.Specifically,we analyzed the effects of drug induced stimulation of lipid drops and endosomes,promotion of cell endocytosis,and induction of cellular senescence on Dark-vacuole bodies.Our results indicate that Dark-vacuole bodies show little correlation with lipid drops and endocytosis vesicles,but are significantly associated with late endosomes.Furthermore,cellular senescence leads to a significant increase in the number and size of Dark-vacuole bodies.This study serves as a foundation for further confirming the nature of Dark-vacuole bodies as new organelles.