Quantitative Phase Information Research Articles

Eagle-Eye Inspired Meta-Device for Phase Imaging.

The dual-focus vision observed in eagles' eyes is an intriguing phenomenon captivates scientists since a long time. Inspired by this natural occurrence, the authors' research introduces a novel bifocal meta-device incorporating a polarized camera capable of simultaneously capturing images for two different polarizations with slightly different focal distances. This innovative approach facilitates the concurrent acquisition of underfocused and overfocused images in a single snapshot, enabling the effective extraction of quantitative phase information from the object using the transport of intensity equation. Experimental demonstrations showcase the application of quantitative phase imaging to artificial objects and human embryonic kidney cells, particularly emphasizing the meta-device's relevance in dynamic scenarios such as laser-induced ablation in human embryonic kidney cells. Moreover, it provides a solution for the quantification during the dynamic process at the cellular level. Notably, the proposed eagle-eye inspired meta-device for phase imaging (EIMPI), due to its simplicity and compact nature, holds promise for significant applications in fields such as endoscopy and headsets, where a lightweight and compact setup is essential.

All-optical complex field imaging using diffractive processors.

Complex field imaging, which captures both the amplitude and phase information of input optical fields or objects, can offer rich structural insights into samples, such as their absorption and refractive index distributions. However, conventional image sensors are intensity-based and inherently lack the capability to directly measure the phase distribution of a field. This limitation can be overcome using interferometric or holographic methods, often supplemented by iterative phase retrieval algorithms, leading to a considerable increase in hardware complexity and computational demand. Here, we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital processing. Our design utilizes successive deep learning-optimized diffractive surfaces that are structured to collectively modulate the input complex field, forming two independent imaging channels that perform amplitude-to-amplitude and phase-to-intensity transformations between the input and output planes within a compact optical design, axially spanning ~100 wavelengths. The intensity distributions of the output fields at these two channels on the sensor plane directly correspond to the amplitude and quantitative phase profiles of the input complex field, eliminating the need for any digital image reconstruction algorithms. We experimentally validated the efficacy of our complex field diffractive imager designs through 3D-printed prototypes operating at the terahertz spectrum, with the output amplitude and phase channel images closely aligning with our numerical simulations. We envision that this complex field imager will have various applications in security, biomedical imaging, sensing and material science, among others.

Noise correction in differential phase contrast for improving phase sensitivity

Differential phase contrast (DPC) imaging relies on computational analysis to extract quantitative phase information from phase gradient images. However, even modest noise level can introduce errors that propagate through the computational process, degrading the quality of the final phase result and further reducing phase sensitivity. Here, we introduce the noise-corrected DPC (ncDPC) to enhance phase sensitivity. This approach is based on a theoretical DPC model that effectively considers most relevant noise sources in the camera and non-uniform illumination in DPC. In particular, the dominating shot noise and readout noise variance can be jointly estimated using frequency analysis and further corrected by block-matching 3D (BM3D) method. Finally, the denoised images are used for phase retrieval based on the common Tikhonov inversion. Our results, based on both simulated and experimental data, demonstrate that ncDPC outperforms the traditional DPC (tDPC), enabling significant improvements in both phase reconstruction quality and phase sensitivity. Besides, we have demonstrated the broad applicability of ncDPC by showing its performance in various experimental datasets.

The virtual staining method by quantitative phase imaging for label free lymphocytes based on self-supervised iteration cycle-consistent adversarial networks.

Quantitative phase imaging (QPI) provides 3D structural and morphological information for label free living cells. Unfortunately, this quantitative phase information cannot meet doctors' diagnostic requirements of the clinical "gold standard," which displays stained cells' pathological states based on 2D color features. To make QPI results satisfy the clinical "gold standard," the virtual staining method by QPI for label free lymphocytes based on self-supervised iteration Cycle-Consistent Adversarial Networks (CycleGANs) is proposed herein. The 3D phase information of QPI is, therefore, trained and transferred to a kind of 2D "virtual staining" image that is well in agreement with "gold standard" results. To solve the problem that unstained QPI and stained "gold standard" results cannot be obtained for the same label free living cell, the self-supervised iteration for the CycleGAN deep learning algorithm is designed to obtain a trained stained result as the ground truth for error evaluation. The structural similarity index of our virtual staining experimental results for 8756 lymphocytes is 0.86. Lymphocytes' area errors after converting to 2D virtual stained results from 3D phase information are less than 3.59%. The mean error of the nuclear to cytoplasmic ratio is 2.69%, and the color deviation from the "gold standard" is less than 6.67%.

Removal of phase residues in electron holography.

Electron holography provides quantitative phase information regarding the electromagnetic fields and the morphology of micro- to nano-scale samples. A phase image reconstructed numerically from an electron hologram sometimes includes phase residues, i.e. origins of unremovable phase discontinuities, which make it much more difficult to quantitatively analyze local phase values. We developed a method to remove the residues in a phase image by a combination of patching local areas of a hologram and denoising based on machine learning. The small patches for a hologram, which were generated using the spatial frequency information of the own fringe patterns, were pasted at each residue point by an algorithm based on sparse modeling. After successive phase reconstruction, the phase components with no dependency on the vicinity were filtered out by Gaussian process regression. We determined that the phase discontinuities that appeared around phase residues were removed and the phase distributions of an atomic resolution phase image of a Pt nanoparticle were sufficiently restored.

Dielectric Metasurface Enabled Compact, Single-Shot Digital Holography for Quantitative Phase Imaging.

Quantitative phase imaging (QPI) enables nondestructive, real-time, label-free imaging of transparent specimens and can reveal information about their fundamental properties such as cell size and morphology, mass density, particle dynamics, and cellular fluctuations. Development of high-performance and low-cost quantitative phase imaging systems is thus required in many fields, including on-site biomedical imaging and industrial inspection. Here, we propose an ultracompact, highly stable interferometer based on a single-layer dielectric metasurface for common path off-axis digital holography and experimentally demonstrate quantitative phase imaging. The interferometric imaging system leveraging an ultrathin multifunctional metasurface captures image plane holograms in a single shot and provides quantitative phase information on the test samples for extraction of its physical properties. With the benefits of planar engineering and high integrability, the proposed metasurface-based method establishes a stable miniaturized QPI system for reliable and cost-effective point-of-care devices, live cell imaging, 3D topography, and edge detection for optical computing.

Multi-derivative method for phase extraction without knowing carrier frequencies in off-axis quantitative phase imaging.

We propose a multi-derivative method to reconstruct the phase of transparent objects in off-axis quantitative phase imaging (QPI). By numerically computing first-, second-, and third-order derivatives of the interferogram, we demonstrate that one can extract the quantitative phase information in a straightforward way, without prior knowledge of the carrier frequencies or Fourier transform. In contrast to existing advanced derivative methods, our approach markedly streamlines the alignment and retrieval processes, all without requiring any special prerequisites. This enhancement seamlessly translates into improved reconstruction quality. Furthermore, when compared to cutting-edge Fourier-division-based methods, our technique distinctly accelerates the phase retrieval speed. We verified our method using white-light diffraction phase microscopy and laser off-axis QPI, and the results indicate that our method can allow a fast, high-quality retrieval with frame rates up to 41.6 fps for one- megapixel interferograms on a regular computer.

A-144 Three-wavelength Polarization Imaging Method for the Analysis of Individual Human Blood Cells With Dye-Enhanced Dispersion of Dilution Buffer

Abstract Background Performing basic hematology analysis in a near-patient setting enables more rapid clinical decision making in the process of diagnosis. Traditional blood analyzers in central labs use a complex combination of sensing principles and chemical modification of the sample to derive the necessary parameters for a complete blood count (CBC). To reduce cost and maintenance, varying approaches have been employed to determine a CBC with a single-step measurement technique. One common option is the use of optical microscopy to deliver highly reliable results, examples are fluorescence microscopy, digital holographic microscopy, or spatial light interference microscopy. However, most of these technologies lack a proper determination of cellular-level RBC parameters. Therefore, a non-interferometric, quantitative-phase measurement technique was developed to derive relevant CBC parameters from a single measurement, comparable to a standard lab analyzer. Methods The optical setup employed by this method enables the parallel acquisition of brightfield and differential interference contrast (DIC) images by polarization-sensitive cameras. By using three cameras with individual optical filters, quantitative phase and absorption information can be calculated, which enables the determination of mean cellular volume (MCV), mean cellular hemoglobin (MCH), and mean cellular hemoglobin concentration (MCHC) for red blood cells (RBCs). The whole-blood sample is diluted in a buffer containing a non-cell-penetrating dye to enhance optical refraction contrast and therefore precision in the dependent calculated quantities. The sample is then introduced to a glass flow cell, and the cells are allowed to sediment to the bottom for imaging. Machine learning is applied to the derived images to classify unstained native white blood cells (WBCs) to yield a 5-part differential, which distinguishes between neutrophil, monocyte, lymphocyte, eosinophil and basophil. The algorithm is trained by imaging FACS-validated, purified WBC subgroups. All blood samples are measured on an ADVIA® 2120 Hematology System for control. Results A one-step, optical CBC hematology system based on quantitative phase and absorption image analysis was developed. The correlations for MCV and MCHC compared to a standard lab CBC analyzer were 0.77 and 0.84. The accuracy for the 5-part differential WBC classification was approximately 0.8. To enable a visual inspection of the classification results, a pseudo-colored image of each cell was generated and uploaded in a review tool. Conclusion With a more-refined, less-expensive optical setup, a high-quality CBC analyzer suitable for use at the point of care can be derived with the presented method. A possible simplification could be the use of low-cost but high-resolution Fourier ptychography microscopy (FPM) technology. Imaging of blood samples on a single-cell level offers the potential for the analysis of even more morphological parameters, for example, disease-related changes in the cell membrane. Also, the adhesion behavior of the WBCs in the glass flow cell could indicate cell activation status and therefore give information about infection processes. *Trademark disclaimer: “ADVIA, and all associated marks are trademarks of Siemens Healthcare Diagnostics, Inc. or its affiliates. All other trademarks and brands are the property of their respective owners.”

Transformer oil quality assessment based on lens-free holographic microscopy

In order to avoid serious problems caused by excessive gas in transformer oil and to realize online monitoring of transformer oil, we propose a method based on lens-free holographic microscopy for transformer oil quality assessment. Using a lens-free device, we can obtain a hologram of the gas bubbles with a large field of view in transformer oil. After the reconstruction, the quantitative phase and shape information of the gas bubbles in the oil can be acquired, thus we can further calculate the parameter of the area, volume, amount, and gas to oil volume ratio of the gas bubbles in transformer oil, which can be used to evaluate the quality of transformer oil effectively. During the experiment, we measured the smallest diameter of the gas bubble in oil was 8 μm. The newly designed method provides a new solution for the assessment of transformer oil quality.

Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network

As a label‐free imaging technique, quantitative phase imaging (QPI) provides optical path length information of transparent specimens for various applications in biology, materials science, and engineering. Multispectral QPI measures quantitative phase information across multiple spectral bands, permitting the examination of wavelength‐specific phase and dispersion characteristics of samples. Herein, the design of a diffractive processor is presented that can all‐optically perform multispectral quantitative phase imaging of transparent phase‐only objects within a snapshot. The design utilizes spatially engineered diffractive layers, optimized through deep learning, to encode the phase profile of the input object at a predetermined set of wavelengths into spatial intensity variations at the output plane, allowing multispectral QPI using a monochrome focal plane array. Through numerical simulations, diffractive multispectral processors are demonstrated to simultaneously perform quantitative phase imaging at 9 and 16 target spectral bands in the visible spectrum. The generalization of these diffractive processor designs is validated through numerical tests on unseen objects, including thin Pap smear images. Due to its all‐optical processing capability using passive dielectric diffractive materials, this diffractive multispectral QPI processor offers a compact and power‐efficient solution for high‐throughput quantitative phase microscopy and spectroscopy.

Self-supervised neural network for phase retrieval in QDPC microscopy.

Quantitative differential phase contrast (QDPC) microscope plays an important role in biomedical research since it can provide high-resolution images and quantitative phase information for thin transparent objects without staining. With weak phase assumption, the retrieval of phase information in QDPC can be treated as a linearly inverse problem which can be solved by Tikhonov regularization. However, the weak phase assumption is limited to thin objects, and tuning the regularization parameter manually is inconvenient. A self-supervised learning method based on deep image prior (DIP) is proposed to retrieve phase information from intensity measurements. The DIP model that takes intensity measurements as input is trained to output phase image. To achieve this goal, a physical layer that synthesizes the intensity measurements from the predicted phase is used. By minimizing the difference between the measured and predicted intensities, the trained DIP model is expected to reconstruct the phase image from its intensity measurements. To evaluate the performance of the proposed method, we conducted two phantom studies and reconstructed the micro-lens array and standard phase targets with different phase values. In the experimental results, the deviation of the reconstructed phase values obtained from the proposed method was less than 10% of the theoretical values. Our results show the feasibility of the proposed methods to predict quantitative phase with high accuracy, and no use of ground truth phase.

Single-shot wavelength-multiplexed phase microscopy under Gabor regime in a regular microscope embodiment

Phase imaging microscopy under Gabor regime has been recently reported as an extremely simple, low cost and compact way to update a standard bright-field microscope with coherent sensing capabilities. By inserting coherent illumination in the microscope embodiment and producing a small defocus distance of the sample at the input plane, the digital sensor records an in-line Gabor hologram of the target sample, which is then numerically post-processed to finally achieve the sample’s quantitative phase information. However, the retrieved phase distribution is affected by the two well-known drawbacks when dealing with Gabor’s regime, that is, coherent noise and twin image disturbances. Here, we present a single-shot technique based on wavelength multiplexing for mitigating these two effects. A multi-illumination laser source (including 3 diode lasers) illuminates the sample and a color digital sensor (conventional RGB color camera) is used to record the wavelength-multiplexed Gabor hologram in a single exposure. The technique is completed by presenting a novel algorithm based on a modified Gerchberg–Saxton kernel to finally retrieve an enhanced quantitative phase image of the sample, enhanced in terms of coherent noise removal and twin image minimization. Experimental validations are performed in a regular Olympus BX-60 upright microscope using a 20X 0.46NA objective lens and considering static (resolution test targets) and dynamic (living spermatozoa) phase samples.

Combining off-axis digital holography with fluorescence for simultaneous fluorescence and quantitative phase imaging.

Off-axis digital holographic microscopy (DHM) is a microscopy technique capable of extracting and translating quantitative phase information (QPI) from the sample into 4D topographical data, which enables the study of phenotypic changes with sub-second temporal resolution. DHM has been widely implemented for long-term live-cell imaging and microelectromechanical systems characterization. However, as a bright field technique, relying on DHM imaging alone can be challenging especially in characterizing sub-cellular structures. Here we report on the design and implementation of a DHM module that can be retrofitted into a commercial dual-deck Olympus IX83 microscope base, transforming the inverted microscope into a hybrid DHM and fluorescence microscopy (DHM-FM) system. In order to achieve simultaneous acquisition of DHM and fluorescence data, the DHM module is designed to integrate illumination, dichroic filter, and interference submodules. The condenser of the microscope was replaced by the DHM illumination module, and the dichroic module was placed in the microscope platform to separate the DHM beam from the fluorescence signal. Finally, the DHM signal was delivered to the interference module outside the microscope body. As the DHM and fluorescence subsystems share the same field of view, this platform can acquire complementary data simultaneously. The capabilities of this coupled platform were explored through a series of pilot studies, including examining phase condensate formation and cellular dynamics. This modular configuration will help others explore the potential of off-axis DHM in their own work and, in particular, the potential of coupled DHM-fluorescence imaging.

Exceeding the limits of algorithmic self-calibrated aberration recovery in Fourier ptychography

Fourier ptychographic microscopy is a computational imaging technique that provides quantitative phase information and high resolution over a large field-of-view. Although the technique presents numerous advantages over conventional microscopy, model mismatch due to unknown optical aberrations can significantly limit reconstruction quality. A practical way of correcting for aberrations without additional data capture is through algorithmic self-calibration, in which a pupil recovery step is embedded into the reconstruction algorithm. However, software-only aberration correction is limited in accuracy. Here, we evaluate the merits of implementing a simple, dedicated calibration procedure for applications requiring high accuracy. In simulations, we find that for a target sample reconstruction error, we can image without any aberration corrections only up to a maximum aberration magnitude of λ/40. When we use algorithmic self-calibration, we can tolerate an aberration magnitude up to λ/10 and with our proposed diffuser calibration technique, this working range is extended further to λ/3. Hence, one can trade off complexity for accuracy by using a separate calibration process, which is particularly useful for larger aberrations.

Quantification of pollen viability in Lantana camara by digital holographic microscopy.

Pollen grains represent the male gametes of seed plants and their viability is critical for sexual reproduction in the plant life cycle. Palynology and viability studies have traditionally been used to address a range of botanical, ecological and geological questions, but recent work has revealed the importance of pollen viability in invasion biology as well. Here, we report an efficient visual method for assessing the viability of pollen using digital holographic microscopy (DHM). Imaging data reveal that quantitative phase information provided by the technique can be correlated with viability as indicated by the outcome of the colorimetric test. We successfully test this method on pollen grains of Lantana camara, a well-known alien invasive plant in the tropical world. Our results show that pollen viability may be assessed accurately without the usual staining procedure and suggest potential applications of the DHM methodology to a number of emerging areas in plant science.

Quantitative phase reconstruction method based on an infrared-like single wave conversion in dual-wavelength phase-shift digital holography

Dual-wavelength digital holography has recently become a promising tool for achieving higher axial measurement ranges in microscopy. However, digital filtering, such as Fourier transform, or a separate hologram acquisition process is essential for retrieving the quantitative phase information of each respective wavelength. In this paper, a quantitative phase reconstruction method based on an infrared-like single wave conversion in dual-wavelength phase-shift digital holography, which does not require any of the processes mentioned above, has been proposed. Based on the proposed method, the quantitative phase information on the axial measurement specimen was simply obtained by only a phase-shift of a quarter of an infrared-like single wave which has the same magnitude of the synthetic wavelength. This approach simplifies not only the hologram acquisition process but also the numerical reconstruction process. The newly proposed theory is verified and evaluated using simulations and experimental validation.

High-resolution polarization-sensitive Fourier ptychography microscopy using a high numerical aperture dome illuminator.

Polarization-sensitive Fourier-ptychography microscopy (pFPM) allows for high resolution imaging while maintaining a large field of view, and without mechanical movements of optical-setup components. In contrast to ordinary light microscopes, pFPM provides quantitative absorption and phase information, for complex and birefringent specimens, with high resolution across a wide field of view. Using a semi-spherical home-built LED illumination array, a single polarizer, and a 10x /0.28NA objective, we experimentally demonstrate high performance pFPM with a synthesized NA of 1.1. Applying the standard quantitative method, a measured half-pitch resolution of 244 nm is achieved for the 1951 USAF resolution test target. As application examples, the polarimetric properties of a herbaceous flowering plant and the metastatic carcinoma of human liver cells are analyzed and quantitatively imaged.

ContransGAN: Convolutional Neural Network Coupling Global Swin-Transformer Network for High-Resolution Quantitative Phase Imaging with Unpaired Data.

Optical quantitative phase imaging (QPI) is a frequently used technique to recover biological cells with high contrast in biology and life science for cell detection and analysis. However, the quantitative phase information is difficult to directly obtain with traditional optical microscopy. In addition, there are trade-offs between the parameters of traditional optical microscopes. Generally, a higher resolution results in a smaller field of view (FOV) and narrower depth of field (DOF). To overcome these drawbacks, we report a novel semi-supervised deep learning-based hybrid network framework, termed ContransGAN, which can be used in traditional optical microscopes with different magnifications to obtain high-quality quantitative phase images. This network framework uses a combination of convolutional operation and multiheaded self-attention mechanism to improve feature extraction, and only needs a few unpaired microscopic images to train. The ContransGAN retains the ability of the convolutional neural network (CNN) to extract local features and borrows the ability of the Swin-Transformer network to extract global features. The trained network can output the quantitative phase images, which are similar to those restored by the transport of intensity equation (TIE) under high-power microscopes, according to the amplitude images obtained by low-power microscopes. Biological and abiotic specimens were tested. The experiments show that the proposed deep learning algorithm is suitable for microscopic images with different resolutions and FOVs. Accurate and quick reconstruction of the corresponding high-resolution (HR) phase images from low-resolution (LR) bright-field microscopic intensity images was realized, which were obtained under traditional optical microscopes with different magnifications.

All‐Optical Phase Recovery: Diffractive Computing for Quantitative Phase Imaging (Advanced Optical Materials 15/2022)

Quantitative phase imaging (QPI) is a label-free computational imaging technique that provides optical path length information of specimens. Modern QPI techniques rely on iterative, time consuming and power-hungry digital reconstruction algorithms to reveal the quantitative phase image of an object. Engineers at UCLA report the design of diffractive networks that can all-optically recover the quantitative phase information of a scene, solely using the diffraction of light through passive engineered surfaces. For further details, see article number 2200281 by Deniz Mengu and Aydogan Ozcan.

Phase-shift digital holography using multilayer ceramic capacitor actuators

In this paper, a four-step phase-shift digital holography (DH) system using multilayer ceramic capacitors (MLCCs) as actuators is proposed. This system is capable of implementing DH with low costs and small-form factors. As a material for actuators, MLCCs are more economical and easier to construct than conventional piezoelectric transducers. By applying a voltage to a single MLCC and measuring the displacement and standard deviation based on the shape change observed on the top surface of the MLCC with laser Doppler velocimetry, the applicability of the MLCC in terms of suitable driving characteristics for use as an actuator in the DH system was confirmed. As the accurate phase step actuation required to employ the MLCC actuator for DH cannot be performed, a four-step hologram extraction method was employed using the intensity information of the hologram obtained by continuously driving the MLCC actuator. Consequently, the proposed MLCC actuator was applied to a typical Michelson interferometer setup to measure a 1.8 µm step sample, and the quantitative phase information was obtained with a standard deviation of less than 1.3 nm.