Optimal illumination scheme for isotropic quantitative differential phase contrast microscopy

Fan, Yao; Sun, Jiasong; Chen, Qian; Pan, Xiangpeng; Tian, Lei; Zuo, Chao

doi:10.1364/prj.7.000890

Cited by 62 publications

(50 citation statements)

References 46 publications

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“…As a result, illumination patterns containing at least two axes of asymmetry are commonly used to ensure complete Fourier coverage. Several studies on the choice of illumination patterns have been performed based on the linear model [18,27]. A CNN-based technique has also been developed to optimize the illumination patterns using a data-driven framework [17].…”

Section: Multiplexed Illumination For Large-sbp Phase Imagingmentioning

confidence: 99%

Reliable deep-learning-based phase imaging with uncertainty quantification

Xue

Cheng

et al. 2019

Optica

Self Cite

155

114

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Emerging deep-learning (DL)-based techniques have significant potential to revolutionize biomedical imaging. However, one outstanding challenge is the lack of reliability assessment in the DL predictions, whose errors are commonly revealed only in hindsight. Here, we propose a new Bayesian convolutional neural network (BNN)based framework that overcomes this issue by quantifying the uncertainty of DL predictions. Foremost, we show that BNN-predicted uncertainty maps provide surrogate estimates of the true error from the network model and measurement itself. The uncertainty maps characterize imperfections often unknown in real-world applications, such as noise, model error, incomplete training data, and out-of-distribution testing data. Quantifying this uncertainty provides a per-pixel estimate of the confidence level of the DL prediction as well as the quality of the model and dataset. We demonstrate this framework in the application of large space-bandwidth product phase imaging using a physics-guided coded illumination scheme. From only five multiplexed illumination measurements, our BNN predicts gigapixel phase images in both static and dynamic biological samples with quantitative credibility assessment. Furthermore, we show that low-certainty regions can identify spatially and temporally rare biological phenomena. We believe our uncertainty learning framework is widely applicable to many DL-based biomedical imaging techniques for assessing the reliability of DL predictions.

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Section: Multiplexed Illumination For Large-sbp Phase Imagingmentioning

confidence: 99%

Reliable deep-learning-based phase imaging with uncertainty quantification

Xue

Cheng

et al. 2019

Optica

Self Cite

155

114

View full text Add to dashboard Cite

show abstract

“…Such devices not only provide additional flexibilities of active illumination and aperture control to realize multi-modal microscopy but result in a bunch of new computational light microscopy approaches. Differential phase contrast (DPC) microscopy is an alternative non-interferometic QPI approach based on asymmetric illumination [79,80]. It recovers high-resolution quantitative phase from intensity differences captured with different illumination patterns created by programmable an LED array.…”

Section: Phase Changesmentioning

confidence: 99%

“…For example, the spatial resolution (mainly including lateral/axial resolution and de-pixelation) is associated with the illumination wavelength, illumination angle, objective numerical aperture (NA), and the pixel size of the detector. For phase retrieval, illumination wavelength [156,174], angle [79,80], aperture modulation [76,86], sensor defocus [168] can all produce phase contrast that allows for non-interferometic quantitative phase recovery. Therefore, if one wants to design a quantitative phase microscopy system for high-resolution single-cell-level labelfree imaging, all these factors should be considered comprehensively to develop an appropriate optical modulation scheme.…”

Section: Computational Light Microscopy: Conceptmentioning

confidence: 99%

Smart Computational Light Microscopes (SCLMs) of Smart Computational Imaging Laboratory (SCILab)

Fan

et al. 2021

Preprint

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Computational microscopy, as a subfield of computational imaging, combines optical manipulation and image algorithmic reconstruction to recover multi-dimensional microscopic images or information of micro-objects. In recent years, the revolution in light-emitting diodes (LEDs), low-cost consumer image sensors, modern digital computers, and smartphones provide fertile opportunities for the rapid development of computational microscopy. Consequently, diverse forms of computational microscopy have been invented, including digital holographic microscopy (DHM), transport of intensity equation (TIE), differential phase contrast (DPC) microscopy, lens-free on-chip holography, and Fourier ptychographic microscopy (FPM). These computational microscopy techniques not only provide high-resolution, label-free, quantitative phase imaging capability but also decipher new and advanced biomedical research and industrial applications. Nevertheless, most computational microscopy techniques are still at an early stage of "proof of concept" or "proof of prototype" (based on commercially available microscope platforms). Translating those concepts to stand-alone optical instruments for practical use is an essential step for the promotion and adoption of computational microscopy by the wider bio-medicine, industry, and education community. In this paper, we present four smart computational light microscopes (SCLMs) developed by our laboratory, i.e., smart computational imaging laboratory (SCILab) of Nanjing University of Science and Technology (NJUST), China. These microscopes are empowered by advanced computational microscopy techniques, including digital holography, TIE, DPC, lensless holography, and FPM, which not only enables multi-modal contrast-enhanced observations for unstained specimens, but also can recover their three-dimensional profiles quantitatively. We introduce their basic principles, hardware configurations, reconstruction algorithms, and software design, quantify their imaging performance, and illustrate their typical applications for cell analysis, medical diagnosis, and microlens characterization.

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“…Note that these are not the only choices for proper permittivity tensor retrieval. Other combinations of patterns are possible, but it would require further investigation to determine the optimal one as that is done in phase imaging community (77,78).…”

Section: D: Transfer Functions and Choice Of Illumination Patternmentioning

confidence: 99%

uPTI: uniaxial permittivity tensor imaging of intrinsic density and anisotropy

Yeh

Ivanov

Guo

et al. 2020

Preprint

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Architecture of biological systems is important for their homeostatic functions and is predictive of pathology. Volumetric imaging of intrinsic density, anisotropy, and 3D orientation of cell and tissue components is informative, but still challenging. These physical properties can be described succinctly by the volumetric distribution of the specimen’s permittivity tensor (PT). We report uniaxial permittivity tensor imaging (uPTI), a novel approach for label-free volumetric imaging with diffraction-limited resolution. uPTI combines the oblique illumination and the polarization imaging with high numerical apertures to encode the specimen’s permittivity tensor into intensity modulations, which are decoded with a novel vector diffraction model and a multi-channel convex optimization. The uPTI volumes of polystyrene beads and laser-fabricated anisotropic glass targets show that 3D uPT measurements are quantitative, with diffraction-limited spatial resolution of 0.23 × 0.23 × 0.8 μm3. Automated 2D and 3D imaging of a mouse brain section reveals anatomy at multiple scales (cm – 250 nm) and demonstrates that uPTI enables analysis of the density, anisotropy, and orientation of axon bundles and single axons. We multiplex uPTI with fluorescence de-convolution microscopy to enable correlative analysis of physical and molecular architecture of iPSC-derived cardiomyocytes. uPTI can be added to an existing widefield microscope as a low cost module. We share our implementation of the forward model and optimization algorithms via open source repository. Collectively, the reported advances in optical design, image formation, reconstruction algorithms, and biological interpretation open multiple avenues to study architecture of organelles, cells and tissue sections, including human cells and tissues that are challenging to label.

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Optimal illumination scheme for isotropic quantitative differential phase contrast microscopy

Cited by 62 publications

References 46 publications

Reliable deep-learning-based phase imaging with uncertainty quantification

Reliable deep-learning-based phase imaging with uncertainty quantification

Smart Computational Light Microscopes (SCLMs) of Smart Computational Imaging Laboratory (SCILab)

uPTI: uniaxial permittivity tensor imaging of intrinsic density and anisotropy

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