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.

AN EXTERNAL STANDARD METHOD OF QUANTITATIVE PHASE ANALYSIS OF THE SAMPLE CONTAINING AN AMORPHOUS PHASE BY X—RAY DIFFRACTION

A new method of the quantitative phase analysis of the sample containing an amorphous phase or a standardless phase by X-ray diffraction is proposed in the paper. The addtion of a reference phase or some analytical phase to the analyzed sample is not required in this method and the experimental results are satisfactory.

AN INCREMENTAL METHOD OF X-RAY DIFFRACTION QUANTITATIVE PHASE ANALYSIS OF SAMPLESCONTAINING AMORPHOUS MATERIAL

A new method for quantitative X-ray diffraction phase analysis of a powder misture has been developed according to Popovic's doping method. The weight fraction of amorphous material in the analysed sample is obtained. For a multicomponent system in which (n-2) pure phases are added into an n-phase compnent sample and theweight fractions of all n phases can be determined by the method. The test results of confirmation agree well with the theory.

Quantitative phase imaging(QPI)through random diffusers using a diffractive optical network

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.

Iterative projection meets sparsity regularization:towards practical single-shot quantitative phase imaging with in-line holography

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.

Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-imagefree phase retrieval from single-shot hologram

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.

Deep-learning-based prediction of living cells mitosis via quantitative phase microscopy

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.

Quantitative Phase Analysis of the Calcium Silicate Slag as the Residue of Extracting Alumina from High-Alumina Fly Ash

Calcium silicate slag is the residue of process of pre-desilication alkali lime sintering applied in the high-alumina fly ash to extract the alumina.The quantitative phase analysis(QPA) of the calcium silicate slag has been performed by the Rietveld method based on the powder X-ray diffraction(XRD) with the aid of noncommercial software GSAS-EXPGUI.A known weight of crystalline internal standard(10% CaF_2) was added to the calcium silicate slag to calculate the fraction of amorphous phase and other crystalline phases on an absolute basis.Besides,the calcium silicate slag was characterized by X-ray fluorescence(XRF) and thermo gravimetric(TG) differential scanning calorimetry(DSC) to test the QPA results and investigate its other characters.Finally,the results show that the amorphous fraction is 17.5%(hereinafter,the percentages refer to the mass fraction),and the major crystalline phases detected in the calcium silicate slag consist of 23.5% Beta-Ca_2 SiO_4,10.0% bredigite,10.3% Ca_3Al_2O_6(C_3A) and 21.6% CaCO_3.

Performance analysis of quantitative phase retrieval method in Zernike phase contrast X-ray microscopy

Since the invention of Zernike phase contrast method in 1930,it has been widely used in optical microscopy and more recently in X-ray microscopy.Considering the image contrast is a mixture of absorption and phase information,we recently have proposed and demonstrated a method for quantitative phase retrieval in Zernike phase contrast X-ray microscopy.In this contribution,we analyze the performance of this method at different photon energies.Intensity images of PMMA samples are simulated at 2.5 keV and 6.2 keV,respectively,and phase retrieval is performed using the proposed method.The results demonstrate that the proposed phase retrieval method is applicable over a wide energy range.For weakly absorbing features,the optimal photon energy is 2.5 keV,from the point of view of image contrast and accuracy of phase retrieval.On the other hand,in the case of strong absorption objects,a higher photon energy is preferred to reduce the error of phase retrieval.These results can be used as guidelines to perform quantitative phase retrieval in Zernike phase contrast X-ray microscopy with the proposed method.

MULTI-PEAK MATCH INTENSITY RATIO METHOD OF QUANTI-TATIVE X-RAY DIFFRACTION PHASE ANALYSIS

A new method for quantitative phase analysis is proposed by using X-ray diffraction multi-peak match intensity ratio. This method can obtain the multi-peak match intensity ratio among each phase in the mixture sample by using all diffraction peak data in the mixture sample X-ray diffraction spectrum and combining the relative intensity distribution data of each phase standard peak in JCPDS card to carry on the least square method regression analysis. It is benefit to improve the precision of quantitative phase analysis that the given single line ratio which is usually adopted is taken the place of the multi-peak match intensity ratio and is used in X-ray diffraction quantitative phase analysis of the mixture sample. By analyzing four-group mixture sample, adopting multi-peak match intensity ratio and X-ray diffraction quantitative phase analysis principle of combining the adiabatic and matrix flushing method, it is tested that the experimental results are identical with theory.

Quantifying Isotibolone in Raw Materials of Tibolone

Tibolone, a synthetic steroid, is used in the treatment of natural or surgical menopause disturbs resultant of estrogenic deficiency. Isotibolone (Δ4-tibolone) is one of the three active metabolic degradation products of tibolone that displays progestagenic effects on carcinoma cell growth and gene regulation. Isotibolone can be present in raw material of tibolone due to some inadequate synthesis or storage. Its presence is necessary to be identified and quantified in active pharmaceutical ingredients (API), before its use in the manufacturing of medicines. After a recent study on the crystal structure determination of isotibolone, quantitative phase analyses of both tibolone and isotibolone in raw materials and tablets became possible to be conducted. X-ray powder diffraction is one recommended tool for this purpose, but it can be highly frustrating due to the extreme peak overlap when conventional laboratory equipments are used. In this work we show that the use of Brazilian Synchrotron Light Source X-ray powder diffraction data and the Rietveld method can be successfully applied to identify and quantify the isotibolone in two samples of tibolone raw materials.

High spatial and temporal resolution synthetic aperture phase microscopy

A new optical microscopy technique,termed high spatial and temporal resolution synthetic aperture phase microscopy(HISTR-SAPM),is proposed to improve the lateral resolution of wide-field coherent imaging.Under plane wave illumination,the resolution is increased by twofold to around 260 nm,while achieving millisecond-level temporal resolution.In HISTR-SAPM,digital micromirror devices are used to actively change the sample illumination beam angle at high speed with high stability.An off-axis interferometer is used to measure the sample scattered complex fields,which are then processed to reconstruct high-resolution phase images.Using HISTR-SAPM,we are able to map the height profiles of subwavelength photonic structures and resolve the period structures that have 198 nm linewidth and 132 nm gap(i.e.,a full pitch of 330 nm).As the reconstruction averages out laser speckle noise while maintaining high temporal resolution,HISTR-SAPM further enables imaging and quantification of nanoscale dynamics of live cells,such as red blood cell membrane fluctuations and subcellular structure dynamics within nucleated cells.We envision that HISTR-SAPM will broadly benefit research in material science and biology.

Optically monitoring and controlling nanoscale topography during semiconductor etching

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.

Integrated self-referencing single shot digital holographic microscope and optical tweezer

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.

Tumor heterogeneity and therapy resistance-implications for future treatments of prostate cancer

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.