We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively lowcarrier frequency holograms-deep learning assisted variational Hilbert quantitative phase imaging(...We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively lowcarrier frequency holograms-deep learning assisted variational Hilbert quantitative phase imaging(DL-VHQPI).The method,incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation,reliably and robustly recovers the quantitative phase information of the test objects.It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system.Compared to the conventional end-to-end networks(without a physical model),the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization.The DL-VHQPI is quantitatively studied by numerical simulation.The live-cell experiment is designed to demonstrate the method's practicality in biological research.The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.展开更多
Quantitative phase imaging(QPI)is a label-free technique that provides optical path length information for transparent specimens,finding utility in biology,materials science,and engineering.Here,we present QPI of a th...Quantitative phase imaging(QPI)is a label-free technique that provides optical path length information for transparent specimens,finding utility in biology,materials science,and engineering.Here,we present QPI of a three-dimensional(3D)stack of phase-only objects using a wavelength-multiplexed diffractive optical processor.Utilizing multiple spatially engineered diffractive layers trained through deep learning,this diffractive processor can transform the phase distributions of multiple two-dimensional objects at various axial positions into intensity patterns,each encoded at a unique wavelength channel.These wavelength-multiplexed patterns are projected onto a single field of view at the output plane of the diffractive processor,enabling the capture of quantitative phase distributions of input objects located at different axial planes using an intensity-only image sensor.Based on numerical simulations,we show that our diffractive processor could simultaneously achieve all-optical QPI across several distinct axial planes at the input by scanning the illumination wavelength.A proof-of-concept experiment with a 3D-fabricated diffractive processor further validates our approach,showcasing successful imaging of two distinct phase objects at different axial positions by scanning the illumination wavelength in the terahertz spectrum.Diffractive network-based multiplane QPI designs can open up new avenues for compact on-chip phase imaging and sensing devices.展开更多
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,Fo...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.展开更多
Quantitative phase imaging(QPI)is a label-free computational imaging technique used in various fields,including biology and medical research.Modern QPI systems typically rely on digital processing using iterative algo...Quantitative phase imaging(QPI)is a label-free computational imaging technique used in various fields,including biology and medical research.Modern QPI systems typically rely on digital processing using iterative algorithms for phase retrieval and image reconstruction.Here,we report a diffractive optical network trained to convert the phase information of input objects positioned behind random diffusers into intensity variations at the output plane,all-optically performing phase recovery and quantitative imaging of phase objects completely hidden by unknown,random phase diffusers.This QPI diffractive network is composed of successive diffractive layers,axially spanning in total~70λ,where is the illumination wavelength;unlike existing digital image reconstruction and phase retrieval methods,it forms an all-optical processor that does not require external power beyond the illumination beam to complete its QPI reconstruction at the speed of light propagation.This all-optical diffractive processor can provide a low-power,high frame rate and compact alternative for quantitative imaging of phase objects through random,unknown diffusers and can operate at different parts of the electromagnetic spectrum for various applications in biomedical imaging and sensing.The presented QPI diffractive designs can be integrated onto the active area of standard CCD/CMOS-based image sensors to convert an existing optical microscope into a diffractive QPI microscope,performing phase recovery and image reconstruction on a chip through light diffraction within passive structured layers.展开更多
Holography provides access to the optical phase.The emerging compressive phase retrieval approach can achieve in-line holographic imaging beyond the information-theoretic limit or even from a single shot by exploring ...Holography provides access to the optical phase.The emerging compressive phase retrieval approach can achieve in-line holographic imaging beyond the information-theoretic limit or even from a single shot by exploring the signal priors.However,iterative projection methods based on physical knowledge of the wavefield suffer from poor imaging quality,whereas the regularization techniques sacrifice robustness for fidelity.In this work,we present a unified compressive phase retrieval framework for in-line holography that encapsulates the unique advantages of both physical constraints and sparsity priors.In particular,a constrained complex total variation(CCTV)regularizer is introduced that explores the well-known absorption and support constraints together with sparsity in the gradient domain,enabling practical high-quality in-line holographic imaging from a single intensity image.We developed efficient solvers based on the proximal gradient method for the non-smooth regularized inverse problem and the corresponding denoising subproblem.Theoretical analyses further guarantee the convergence of the algorithms with prespecified parameters,obviating the need for manual parameter tuning.As both simulated and optical experiments demonstrate,the proposed CCTV model can characterize complex natural scenes while utilizing physically tractable constraints for quality enhancement.This new compressive phase retrieval approach can be extended,with minor adjustments,to various imaging configurations,sparsifying operators,and physical knowledge.It may cast new light on both theoretical and empirical studies.展开更多
Quantitative phase imaging(QPI)enables non-invasive cellular analysis by utilizing cell thickness and refractive index as intrinsic probes,revolutionizing label-free microscopy in cellular research.Differential phase ...Quantitative phase imaging(QPI)enables non-invasive cellular analysis by utilizing cell thickness and refractive index as intrinsic probes,revolutionizing label-free microscopy in cellular research.Differential phase contrast(DPC),a non-interferometric QPI technique,requires only four intensity images under asymmetric illumination to recover the phase of a sample,offering the advantages of being label-free,non-coherent and highly robust.Its phase reconstruction result relies on precise modeling of the phase transfer function(PTF).However,in real optical systems,the PTF will deviate from its theoretical ideal due to the unknown wavefront aberrations,which will lead to significant artifacts and distortions in the reconstructed phase.We propose an aberration-corrected DPC(ACDPC)method that utilizes three intensity images under annular illumination to jointly retrieve the aberration and the phase,achieving high-quality QPI with minimal raw data.By employing three annular illuminations precisely matched to the numerical aperture of the objective lens,the object information is transmitted into the acquired intensity with a high signal-to-noise ratio.Phase retrieval is achieved by an iterative deconvolution algorithm that uses simulated annealing to estimate the aberration and further employs regularized deconvolution to reconstruct the phase,ultimately obtaining a refined complex pupil function and an aberration-corrected quantitative phase.We demonstrate that ACDPC is robust to multi-order aberrations without any priori knowledge,and can effectively retrieve and correct system aberrations to obtain high-quality quantitative phase.Experimental results show that ACDPC can clearly reproduce subcellular structures such as vesicles and lipid droplets with higher resolution than conventional DPC,which opens up new possibilities for more accurate subcellular structure analysis in cell biology.展开更多
We present a deep learning approach for living cells mitosis classification based on label-free quantitative phase imaging with transport of intensity equation methods.In the approach,we applied a pretrained deep conv...We present a deep learning approach for living cells mitosis classification based on label-free quantitative phase imaging with transport of intensity equation methods.In the approach,we applied a pretrained deep convolutional neural network using transfer learning for binary classification of mitosis and non-mitosis.As a validation,we demonstrated the performances of the network trained by phase images and intensity images,respectively.The convolutional neural network trained by phase images achieved an average accuracy of 98.9%on the validation data,which outperforms the average accuracy 89.6%obtained by the network trained by intensity images.We believe that the quantitative phase microscopy in combination with deep learning enables researchers to predict the mitotic status of living cells noninvasively and efficiently.展开更多
Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,...Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.展开更多
Red blood cell(RBC)indices serve as clinically important parameters for diagnosing various blood-related diseases.Conventional hematology analyzers provide the highly accurate detection of RBC indices but require larg...Red blood cell(RBC)indices serve as clinically important parameters for diagnosing various blood-related diseases.Conventional hematology analyzers provide the highly accurate detection of RBC indices but require large blood volumes(>1 mL),and the results are bulk mean values averaged over a large number of RBCs.Moreover,they do not provide quantitative information related to the morphological and chemical alteration of RBCs at the single-cell level.Recently,quantitative phase imaging(QPI)methods have been introduced as viable detection platforms for RBC indices.However,coherent QPI methods are built on complex optical setups and suffer from coherent speckle noise,which limits their detection accuracy and precision.Here,we present spectroscopic differential phase-contrast(sDPC)microscopy as a platform for measuring RBC indices.sDPC is a computational microscope that produces color-dependent phase images with higher spatial resolution and reduced speckle noise compared to coherent QPIs.Using these spectroscopic phase images and computational algorithms,RBC indices can be extracted with high accuracy.We experimentally demonstrate that sDPC enables the high-accuracy measurement of the mean corpuscular hemoglobin concentration,mean corpuscular volume,mean corpuscular hemoglobin,red cell distribution width,hematocrit,hemoglobin concentration,and RBC count with errors smaller than 7%as compared to a clinical hematology analyzer based on flow cytometry(XN-2000;Sysmex,Kobe,Japan).We further validate the clinical utility of the sDPC method by measuring and comparing the RBC indices of the control and anemic groups against those obtained using the clinical hematology analyzer.展开更多
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 rec...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.展开更多
We present epi-diffraction phase microscopy(epi-DPM)as a non-destructive optical method for monitoring semiconductor fabrication processes in real time and with nanometer level sensitivity.The method uses a compact M...We present epi-diffraction phase microscopy(epi-DPM)as a non-destructive optical method for monitoring semiconductor fabrication processes in real time and with nanometer level sensitivity.The method uses a compact Mach–Zehnder interferometer to recover quantitative amplitude and phase maps of the field reflected by the sample.The low temporal noise of 0.6 nm per pixel at 8.93 frames per second enabled us to collect a three-dimensional movie showing the dynamics of wet etching and thereby accurately quantify non-uniformities in the etch rate both across the sample and over time.By displaying a gray-scale digital image on the sample with a computer projector,we performed photochemical etching to define arrays of microlenses while simultaneously monitoring their etch profiles with epi-DPM.展开更多
Digital holographic microscopy is a single-shot technique for quantitative phase imaging of samples,yielding thickness profiles of phase objects.It provides sample features based on their morphology,leading to their c...Digital holographic microscopy is a single-shot technique for quantitative phase imaging of samples,yielding thickness profiles of phase objects.It provides sample features based on their morphology,leading to their classification and identification.However,observing samples,especially cells,in fluids using holographic microscopes is difficult without immobilizing the object.Optical tweezers can be used for sample immobilization in fluids.The present manuscript provides an overview of our ongoing work on the development of a compact,low-cost microscopy system for digital holographic imaging of optically trapped samples.Integration of digital holographic microscopy system with tweezers is realized by using the optical pickup unit extracted from DVD burners to trap microsamples,which are then holographically imaged using a highly compact self-referencing interferometer along with a low-cost,in-house developed quadrant photodiode,providing morphological and spectral information of trapped particles.The developed integrated module was tested using polystyrene microspheres as well as human erythrocytes.The investigated system offers a multitude of sample features,including physical and mechanical parameters and corner frequency information of the sample.These features were used for sample classification.The proposed technique has vast potential in opening up new avenues for low-cost,digital holographic imaging and analysis of immobilized samples in fluids and their classification.展开更多
Aim:To develop new therapies for prostate cancer,disease heterogeneity must be addressed.This includes patient variation,multi-focal disease,cellular heterogeneity,genomic changes and epigenetic modification.This requ...Aim:To develop new therapies for prostate cancer,disease heterogeneity must be addressed.This includes patient variation,multi-focal disease,cellular heterogeneity,genomic changes and epigenetic modification.This requires more representative models to be used in more innovative ways.Methods:This study used a panel of cell lines and primary prostate epithelial cell cultures derived from patient tissue.Several assays were used;alamar blue,colony forming assays,γH2AX and Ki67 immunofluorescence and comet assays.Ptychographic quantitative phase imaging(QPI),a label-free imaging technique,combined with Cell Analysis Toolbox software,was implemented to carry out real-time analysis of cells and to retrieve morphological,kinetic and population data.Results:A combination of radiation and Vorinostat may be more effective than radiation alone.Primary prostate cancer stem-like cells are more resistant to etoposide than more differentiated cells.Analysis of QPI images showed that cell lines and primary cells differ in their size,motility and proliferation rate.A QPI signature was developed in order to identify two subpopulations of cells within a heterogeneous primary culture.Conclusion:Use of primary prostate epithelial cultures allows assessment of therapies whilst taking into account cellular heterogeneity.Analysis of rare cell populations and embracing novel techniques may ultimately lead to identifying and overcoming treatment resistance.展开更多
基金We are grateful for financial supports from the National Natural Science Foundation of China(61905115,62105151,62175109,U21B2033,62227818)Leading Technology of Jiangsu Basic Research Plan(BK20192003)+5 种基金Youth Foundation of Jiangsu Province(BK20190445,BK20210338)Biomedical Competition Foundation of Jiangsu Province(BE2022847)Key National Industrial Technology Cooperation Foundation of Jiangsu Province(BZ2022039)Fundamental Research Funds for the Central Universities(30920032101)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(JSGP202105,JSGP202201)National Science Center,Poland(2020/37/B/ST7/03629).The authors thank F.Sun for her contribution to this paper in terms of language expression and grammatical correction.
文摘We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively lowcarrier frequency holograms-deep learning assisted variational Hilbert quantitative phase imaging(DL-VHQPI).The method,incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation,reliably and robustly recovers the quantitative phase information of the test objects.It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system.Compared to the conventional end-to-end networks(without a physical model),the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization.The DL-VHQPI is quantitatively studied by numerical simulation.The live-cell experiment is designed to demonstrate the method's practicality in biological research.The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.
基金supported by the U.S.Department of Energy,Office of Basic Energy Sciences,Division of Materials Sciences and Engineering(Grant No.DE-SC0023088).
文摘Quantitative phase imaging(QPI)is a label-free technique that provides optical path length information for transparent specimens,finding utility in biology,materials science,and engineering.Here,we present QPI of a three-dimensional(3D)stack of phase-only objects using a wavelength-multiplexed diffractive optical processor.Utilizing multiple spatially engineered diffractive layers trained through deep learning,this diffractive processor can transform the phase distributions of multiple two-dimensional objects at various axial positions into intensity patterns,each encoded at a unique wavelength channel.These wavelength-multiplexed patterns are projected onto a single field of view at the output plane of the diffractive processor,enabling the capture of quantitative phase distributions of input objects located at different axial planes using an intensity-only image sensor.Based on numerical simulations,we show that our diffractive processor could simultaneously achieve all-optical QPI across several distinct axial planes at the input by scanning the illumination wavelength.A proof-of-concept experiment with a 3D-fabricated diffractive processor further validates our approach,showcasing successful imaging of two distinct phase objects at different axial positions by scanning the illumination wavelength in the terahertz spectrum.Diffractive network-based multiplane QPI designs can open up new avenues for compact on-chip phase imaging and sensing devices.
基金supported by the National Natural Science Foundation of China(61905115,62105151,62175109,U21B2033,62105156)Leading Technology of Jiangsu Basic Research Plan(BK20192003),Youth Foundation of Jiangsu Province(BK20190445,BK20210338)+1 种基金Fundamental Research Funds for the Central Universities(30920032101)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(JSGP202105,JSGP202201).
文摘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.
文摘Quantitative phase imaging(QPI)is a label-free computational imaging technique used in various fields,including biology and medical research.Modern QPI systems typically rely on digital processing using iterative algorithms for phase retrieval and image reconstruction.Here,we report a diffractive optical network trained to convert the phase information of input objects positioned behind random diffusers into intensity variations at the output plane,all-optically performing phase recovery and quantitative imaging of phase objects completely hidden by unknown,random phase diffusers.This QPI diffractive network is composed of successive diffractive layers,axially spanning in total~70λ,where is the illumination wavelength;unlike existing digital image reconstruction and phase retrieval methods,it forms an all-optical processor that does not require external power beyond the illumination beam to complete its QPI reconstruction at the speed of light propagation.This all-optical diffractive processor can provide a low-power,high frame rate and compact alternative for quantitative imaging of phase objects through random,unknown diffusers and can operate at different parts of the electromagnetic spectrum for various applications in biomedical imaging and sensing.The presented QPI diffractive designs can be integrated onto the active area of standard CCD/CMOS-based image sensors to convert an existing optical microscope into a diffractive QPI microscope,performing phase recovery and image reconstruction on a chip through light diffraction within passive structured layers.
基金the National Natural Science Foundation of China(Grant No.61827825)for financial support.
文摘Holography provides access to the optical phase.The emerging compressive phase retrieval approach can achieve in-line holographic imaging beyond the information-theoretic limit or even from a single shot by exploring the signal priors.However,iterative projection methods based on physical knowledge of the wavefield suffer from poor imaging quality,whereas the regularization techniques sacrifice robustness for fidelity.In this work,we present a unified compressive phase retrieval framework for in-line holography that encapsulates the unique advantages of both physical constraints and sparsity priors.In particular,a constrained complex total variation(CCTV)regularizer is introduced that explores the well-known absorption and support constraints together with sparsity in the gradient domain,enabling practical high-quality in-line holographic imaging from a single intensity image.We developed efficient solvers based on the proximal gradient method for the non-smooth regularized inverse problem and the corresponding denoising subproblem.Theoretical analyses further guarantee the convergence of the algorithms with prespecified parameters,obviating the need for manual parameter tuning.As both simulated and optical experiments demonstrate,the proposed CCTV model can characterize complex natural scenes while utilizing physically tractable constraints for quality enhancement.This new compressive phase retrieval approach can be extended,with minor adjustments,to various imaging configurations,sparsifying operators,and physical knowledge.It may cast new light on both theoretical and empirical studies.
基金supported by the National Natural Science Foundation of China(62305162,62227818,62361136588)China Postdoctoral Science Foundation(2023TQ0160,2023M731683)+5 种基金Nanjing University of Science and Technology independent research project(30923010305)National Key Research and Development Program of China(2024YFE0101300)Biomedical Competition Foundation of Jiangsu Province(BE2022847)Key National Industrial Technology Cooperation Foundation of Jiangsu Province(BZ2022039)Fundamental Research Funds for the Central Universities(2023102001)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(JSGP202105,JSGP202201,JSGPCXZNGZ202401)。
文摘Quantitative phase imaging(QPI)enables non-invasive cellular analysis by utilizing cell thickness and refractive index as intrinsic probes,revolutionizing label-free microscopy in cellular research.Differential phase contrast(DPC),a non-interferometric QPI technique,requires only four intensity images under asymmetric illumination to recover the phase of a sample,offering the advantages of being label-free,non-coherent and highly robust.Its phase reconstruction result relies on precise modeling of the phase transfer function(PTF).However,in real optical systems,the PTF will deviate from its theoretical ideal due to the unknown wavefront aberrations,which will lead to significant artifacts and distortions in the reconstructed phase.We propose an aberration-corrected DPC(ACDPC)method that utilizes three intensity images under annular illumination to jointly retrieve the aberration and the phase,achieving high-quality QPI with minimal raw data.By employing three annular illuminations precisely matched to the numerical aperture of the objective lens,the object information is transmitted into the acquired intensity with a high signal-to-noise ratio.Phase retrieval is achieved by an iterative deconvolution algorithm that uses simulated annealing to estimate the aberration and further employs regularized deconvolution to reconstruct the phase,ultimately obtaining a refined complex pupil function and an aberration-corrected quantitative phase.We demonstrate that ACDPC is robust to multi-order aberrations without any priori knowledge,and can effectively retrieve and correct system aberrations to obtain high-quality quantitative phase.Experimental results show that ACDPC can clearly reproduce subcellular structures such as vesicles and lipid droplets with higher resolution than conventional DPC,which opens up new possibilities for more accurate subcellular structure analysis in cell biology.
基金the National Natural Science Foundation of China(NSFC)(No.61927810)the Joint Fund of National Natural Science Foundation ofChina and China Academy of Engineering Physics(NSAF)(No.U1730137)the Fundamental Research Funds for the Central Universities(No.3102019ghxm018)。
文摘We present a deep learning approach for living cells mitosis classification based on label-free quantitative phase imaging with transport of intensity equation methods.In the approach,we applied a pretrained deep convolutional neural network using transfer learning for binary classification of mitosis and non-mitosis.As a validation,we demonstrated the performances of the network trained by phase images and intensity images,respectively.The convolutional neural network trained by phase images achieved an average accuracy of 98.9%on the validation data,which outperforms the average accuracy 89.6%obtained by the network trained by intensity images.We believe that the quantitative phase microscopy in combination with deep learning enables researchers to predict the mitotic status of living cells noninvasively and efficiently.
基金National Natural Science Foundation of China (62275267, 62335018, 12127805, 62105359)National Key Research and Development Program of China (2021YFF0700303, 2022YFE0100700)Youth Innovation Promotion Association, CAS (2021401)
文摘Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.
基金supported by the Samsung Research Funding&Incubation Center of Samsung Electronics under Project SRFC-IT2002-07the National Research Foundation of Korea(NRF)Grant funded by the government of Korea(MSIT)(2023R1A2C3004040)+3 种基金the Korea Institute for Advancement of Technology(KIAT)Grant funded by the government of Korea(MOTIE)(P0019784)the Ministry of Science and ICT(Project 2023-22030004-20)(NTIS,1711179106)the Commercialization Promotion Agency for R&D Outcomes(COMPA)funded by the Ministry of Science and ICT(MSIT)(1711198541,development of key optical technologies of inspection and measurement for the analysis of 3D complex nanostructures)the Korea Medical Device Development Fund(KMDF_PR_20200901_0099,Project Number:9991007255).
文摘Red blood cell(RBC)indices serve as clinically important parameters for diagnosing various blood-related diseases.Conventional hematology analyzers provide the highly accurate detection of RBC indices but require large blood volumes(>1 mL),and the results are bulk mean values averaged over a large number of RBCs.Moreover,they do not provide quantitative information related to the morphological and chemical alteration of RBCs at the single-cell level.Recently,quantitative phase imaging(QPI)methods have been introduced as viable detection platforms for RBC indices.However,coherent QPI methods are built on complex optical setups and suffer from coherent speckle noise,which limits their detection accuracy and precision.Here,we present spectroscopic differential phase-contrast(sDPC)microscopy as a platform for measuring RBC indices.sDPC is a computational microscope that produces color-dependent phase images with higher spatial resolution and reduced speckle noise compared to coherent QPIs.Using these spectroscopic phase images and computational algorithms,RBC indices can be extracted with high accuracy.We experimentally demonstrate that sDPC enables the high-accuracy measurement of the mean corpuscular hemoglobin concentration,mean corpuscular volume,mean corpuscular hemoglobin,red cell distribution width,hematocrit,hemoglobin concentration,and RBC count with errors smaller than 7%as compared to a clinical hematology analyzer based on flow cytometry(XN-2000;Sysmex,Kobe,Japan).We further validate the clinical utility of the sDPC method by measuring and comparing the RBC indices of the control and anemic groups against those obtained using the clinical hematology analyzer.
基金supported by the National Natural Science Foundation of China(61905115)Leading Technology of Jiangsu Basic Research Plan(BK20192003)+3 种基金National Defense Science and Technology Foundation of China(2019-JCJQ-JJ-381)Youth Foundation of Jiangsu Province(BK20190445)Fundamental Research Funds for the Central Universities(30920032101)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(3091801410411).
文摘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.
基金The authors thank Hoa Pham for assistance with the power spectral density calculations and Brian Cunningham,Xiuling Li,Logan Liu and Daniel Wasserman for helpful discussions. This work is supported by NSF CBET-1040462 MRI award.
文摘We present epi-diffraction phase microscopy(epi-DPM)as a non-destructive optical method for monitoring semiconductor fabrication processes in real time and with nanometer level sensitivity.The method uses a compact Mach–Zehnder interferometer to recover quantitative amplitude and phase maps of the field reflected by the sample.The low temporal noise of 0.6 nm per pixel at 8.93 frames per second enabled us to collect a three-dimensional movie showing the dynamics of wet etching and thereby accurately quantify non-uniformities in the etch rate both across the sample and over time.By displaying a gray-scale digital image on the sample with a computer projector,we performed photochemical etching to define arrays of microlenses while simultaneously monitoring their etch profiles with epi-DPM.
基金The work was supported by research grants SERB(EMR/20l7/002724),DAE-BRNS(2013/34/11/BRNS/504),DST-FIST and DST-PURSE.AA and VC would like to acknowledge Abdus Salam International center for Theoretical Physics(ICTP),Trieste,Italy for Regular Associate fellowship.
文摘Digital holographic microscopy is a single-shot technique for quantitative phase imaging of samples,yielding thickness profiles of phase objects.It provides sample features based on their morphology,leading to their classification and identification.However,observing samples,especially cells,in fluids using holographic microscopes is difficult without immobilizing the object.Optical tweezers can be used for sample immobilization in fluids.The present manuscript provides an overview of our ongoing work on the development of a compact,low-cost microscopy system for digital holographic imaging of optically trapped samples.Integration of digital holographic microscopy system with tweezers is realized by using the optical pickup unit extracted from DVD burners to trap microsamples,which are then holographically imaged using a highly compact self-referencing interferometer along with a low-cost,in-house developed quadrant photodiode,providing morphological and spectral information of trapped particles.The developed integrated module was tested using polystyrene microspheres as well as human erythrocytes.The investigated system offers a multitude of sample features,including physical and mechanical parameters and corner frequency information of the sample.These features were used for sample classification.The proposed technique has vast potential in opening up new avenues for low-cost,digital holographic imaging and analysis of immobilized samples in fluids and their classification.
基金funded by a PCUK Innovation Award-RIA15-ST2-022.SK was supported by a White Rose Fund studentship.
文摘Aim:To develop new therapies for prostate cancer,disease heterogeneity must be addressed.This includes patient variation,multi-focal disease,cellular heterogeneity,genomic changes and epigenetic modification.This requires more representative models to be used in more innovative ways.Methods:This study used a panel of cell lines and primary prostate epithelial cell cultures derived from patient tissue.Several assays were used;alamar blue,colony forming assays,γH2AX and Ki67 immunofluorescence and comet assays.Ptychographic quantitative phase imaging(QPI),a label-free imaging technique,combined with Cell Analysis Toolbox software,was implemented to carry out real-time analysis of cells and to retrieve morphological,kinetic and population data.Results:A combination of radiation and Vorinostat may be more effective than radiation alone.Primary prostate cancer stem-like cells are more resistant to etoposide than more differentiated cells.Analysis of QPI images showed that cell lines and primary cells differ in their size,motility and proliferation rate.A QPI signature was developed in order to identify two subpopulations of cells within a heterogeneous primary culture.Conclusion:Use of primary prostate epithelial cultures allows assessment of therapies whilst taking into account cellular heterogeneity.Analysis of rare cell populations and embracing novel techniques may ultimately lead to identifying and overcoming treatment resistance.
