Fast digital refocusing and depth of field extended Fourier ptychography microscopy

Zhang, Shaohui; Zhou, Guocheng; Zheng, Chao; Li, Tong; Hu, Yao; Hao, Qun

doi:10.1364/boe.433033

Cited by 17 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Fig. 4(f2) and 4(g2), the comparable results are reconstructed without the aberration correction except for an auto-refocusing algorithm [58] to remove the effect of defocus. It can be seen that the subcellular structures with a size The reconstructed results with full 121 images using the EPRY method.…”

Section: Ao-qpi On Hela Cell Cultures For a Full-field Imagingmentioning

confidence: 98%

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

et al. 2022

View full text Add to dashboard Cite

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 environment-dependent, 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 $$655\,nm$$ 655 n m at a wavelength of $$525\,nm$$ 525 n m ) across a wide field of view ($$1.77\,mm^2$$ 1.77 m m 2 ) over an extended period of time.

show abstract

Section: Ao-qpi On Hela Cell Cultures For a Full-field Imagingmentioning

confidence: 98%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In Fig. 4(f2) and 4(g2), the comparable results are reconstructed without the aberration correction except for an auto-refocusing algorithm [57] to remove the effect of defocus. It can be seen that the subcellular structures with the size close to the diffraction limit have been completely smoothed out due to the uncorrected aberrations.…”

Section: Ao-qpi On a Quantitative Phase Microscopy Targetmentioning

confidence: 98%

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

Shu

Sun

Fan

et al. 2022

Preprint

View full text Add to dashboard Cite

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 environment-dependent, 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 filed of view (1.77 mm 2 ) over an extended period of time.

show abstract

“…In order to get better optimization results and faster speed, we should use more priori constraints and more accurate imaging models, as we discussed in this chapter on the way of pupil function modeling. We are considering that using the defocus distance calculated by the geometric relationship in the imaging process [26] as the initial parameter of FPMN, and calculating the fluctuation of LEDs at different angles by diffractive optics knowlodge as the initial parameter of FPMN. That may speed up the optimization [32] of FPMN and get better decoulping effect.…”

Section: Conclusion and Disscusionmentioning

confidence: 99%

“…Zhao, Ming et al have tried modeling the LED array position deviation into neural network, but the optimization process is very time-consuming [25]. Since using physical means to reduce the morbidity of inverse problems is a very effective method [26,27,28], we have reduced the pathologicality of phase recovery problems through inserting a wedge angle in front of the microscope to imporve the speed and quality of reconstruction [29].In this paper, in order to reduce the pathological degree of reconstruction problem, we turn to find physical method to correct LED array position deviation. We choose four brightfield to darkfield transition LR images which located on orthogonal direction, using boundary of bright-field to dark-field transition on the LR images to calculate LED array position deviation inspired by the method proposed in reference [3].…”

Section: Introductionmentioning

confidence: 99%

Fourier ptychography multi-parameter neural network with composite physical priori optimization

Yang¹,

Zhang²,

Zheng³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Fourier ptychography microscopy(FP) is a recently developed computational imaging approach for microscopic super-resolution imaging. By turning on each light-emitting-diode (LED) located on different position on the LED array sequentially and acquiring the corresponding images that contain different spatial frequency components, high spatial resolution and quantitative phase imaging can be achieved in the case of large field-of-view. Nevertheless, FPM has high requirements for the system construction and data acquisition processes, such as precise LEDs position, accurate focusing and appropriate exposure time, which brings many limitations to its practical applications. In this paper, inspired by artificial neural network, we propose a Fourier ptychography multi-parameter neural network (FPMN) with composite physical prior optimization. A hybrid parameter determination strategy combining physical imaging model and data-driven network training is proposed to recover the multi layers of the network corresponding to different physical parameters, including sample complex function, system pupil function, defocus distance, LED array position deviation and illumination intensity fluctuation, etc. Among these parameters, LED array position deviation is recovered based on the features of brightfield to darkfield transition low-resolution images while the others are recovered in the process of training of the neural network. The feasibility and effectiveness of FPMN

show abstract

Fast digital refocusing and depth of field extended Fourier ptychography microscopy

Cited by 17 publications

References 29 publications

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

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

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

Fourier ptychography multi-parameter neural network with composite physical priori optimization

Contact Info

Product

Resources

About