Fresnel incoherent compressive holography toward 3D videography via dual-channel simultaneous phase-shifting interferometry.

Fresnel incoherent correlation holography (FINCH) enables high-resolution 3D imaging of objects from several 2D holograms under incoherent light and has many attractive applications in motionless 3D fluorescence imaging. However, FINCH has difficulty implementing 3D imaging of dynamic scenes since multiple phase-shifting holograms need to be recorded for removing the bias term and twin image in the reconstructed scene, which requires the object to remain static during this progress. Here, we propose a dual-channel Fresnel noncoherent compressive holography method. First, a pair of holograms with π phase shifts obtained in a single shot are used for removing the bias term noise. Then, a physic-driven compressive sensing (CS) algorithm is used to achieve twin-image-free reconstruction. In addition, we analyze the reconstruction effect and suitability of the CS algorithm and two-step phase-shift filtering algorithm for objects with different complexities. The experimental results show that the proposed method can record hologram videos of 3D dynamic objects and scenes without sacrificing the imaging field of view or resolution. Moreover, the system refocuses images at arbitrary depth positions via computation, hence providing a new method for fast high-throughput incoherent 3D imaging.

Adaptive phase-shifting interferometry based on a phase-shifting digital holography algorithm.

Phase-shifting interferometry (PSI) requires accurate phase shifts between interferograms for realizing high-accuracy phase retrieval. This paper presents an adaptive PSI through synchronously capturing phase shifts measurement interferograms and phase measurement interferograms, in which the former is a series of spatial carrier frequency phase-shifting interferograms generated by an additional assembly and the phase shifts are calculated with the single-spectrum phase shifts measurement algorithm (SS-PSMA), the latter is employed for phase retrieval with an adaptive phase-shifting digital holography algorithm (PSDHA) based on complex amplitude recovery. In addition to exhibiting excellent reliability, high-accuracy phase retrieval (0.02 rad), and short calculation time (<25 ms), the proposed adaptive PSDHA is suitable for various interferograms with different fringe shapes and numbers. Importantly, both simulation analysis and experimental result demonstrate that this adaptive PSI based on PSDHA can effectively eliminate phase-shifting errors caused by phase shifter and external disturbance, ensuring high-accuracy phase shifts measurement and phase retrieval, meanwhile significantly reducing phase-shifting interferograms acquisition time and phase retrieval calculation time.

Convenient dual-wavelength digital holography based on orthogonal polarization strategy with a Wollaston prism.

Dual-wavelength digital holography effectively expands the measurement range of digital holography, but it increases the complexity of optical system due to non-common-path of two wavelengths. Here, by using orthogonal polarization strategy, we present a dual-wavelength digital holography based on a Wollaston prism (DWDH-WP) to separate the reference beams of two wavelengths and realize the common-path of two wavelengths. A Wollaston prism is inset into the reference beam path of the off-axis digital holography system, so two orthogonal-polarized reference beams of two different wavelengths separated at different directions are generated. Then a dual-wavelength multiplexed interferogram with orthogonal interference fringes is captured by using a monochrome camera, in which both the polarization orientations and the interference fringe orientations of two wavelengths are orthogonal, so the spectral crosstalk of two wavelengths with arbitrary wavelength difference can be avoided. Compared with the existing DWDH method, the proposed DWDH-WP method can conveniently realize the common-path of the reference beams of two wavelengths, so it reveals obvious advantages in spectral separation, spectral crosstalk, system simplification, and adjustment flexibility. Both effectiveness and flexibility of the proposed DWDH-WP method are demonstrated by the phase measurement of the HeLa cell and vortex phase plate.

Incidence and risk factors of post-operative delirium in glioma patients: A prospective cohort study in general wards.

AIMS: Glioma patients are at high risk for postoperative delirium (POD), yet studies focusing on this population in general neurosurgical ward settings are limited. This paper investigates the incidence of POD and related risk factors in glioma patients hospitalized in general wards. DESIGN: Prospective observational study. METHODS: This prospective study included 133 adult glioma patients hospitalized in the general neurosurgery ward. In addition to collecting routine perioperative general clinical data, patients' psychological status was assessed preoperatively using the Hospital Anxiety and Depression Scale (HADS). POD was assessed within 3 days postoperatively using the Confusion of Consciousness Assessment method, twice daily. The incidence of POD was calculated, and risk factors were identified using logistic regression analysis. RESULTS: The incidence of POD in glioma patients admitted to the general ward was 31.6% (40/133). Multivariate regression revealed advanced age (age > 50 years), frontal lobe tumour, presence of preoperative anxiety or depression, retention of a luminal drain, postoperative pain, indwelling catheter these six factors were independent risk factors for the development of delirium in patients after surgery. CONCLUSION: In general ward settings, supratentorial glioma patients exhibit a high risk of POD. Critical risk factors include preoperative psychological conditions, as well as postoperative pain, drainage and catheterization. Rigorous preoperative evaluations, effective pain management strategies and the integration of humanistic care principles are essential in mitigating the risk of POD for glioma patients. RELEVANCE TO CLINICAL PRACTICE: In general ward settings, this study reveals the high occurrence of POD in glioma patients and identifies preoperative psychological states, age, tumour location and several postoperative factors as significant risk factors for POD, which provides a framework for targeted interventions. By integrating these insights into clinical practice, healthcare teams can better identify glioma patients at risk for POD and implement preventive measures, thereby enhancing recovery and overall care quality for glioma patients in general neurosurgical wards. REPORTING METHOD: This study adheres to the STROBE guidelines, ensuring a transparent and comprehensive reporting of the observational research methodology and results. PATIENT OR PUBLIC CONTRIBUTION: Patients involvement was limited to the provision of data through their participation in the study's assessments and the collection of clinical information. The study did not involve a direct patient or public contribution in the design, conduct, analysis, or interpretation of the data, nor in the preparation of the manuscript.

Single-shot Fresnel incoherent correlation holography via deep learning based phase-shifting technology.

Fresnel incoherent correlation holography (FINCH) realizes non-scanning three-dimension (3D) images using spatial incoherent illumination, but it requires phase-shifting technology to remove the disturbance of the DC term and twin term that appears in the reconstruction field, thus increasing the complexity of the experiment and limits the real-time performance of FINCH. Here, we propose a single-shot Fresnel incoherent correlation holography via deep learning based phase-shifting (FINCH/DLPS) method to realize rapid and high-precision image reconstruction using only a collected interferogram. A phase-shifting network is designed to implement the phase-shifting operation of FINCH. The trained network can conveniently predict two interferograms with the phase shift of 2/3 π and 4/3 π from one input interferogram. Using the conventional three-step phase-shifting algorithm, we can conveniently remove the DC term and twin term of the FINCH reconstruction and obtain high-precision reconstruction through the back propagation algorithm. The Mixed National Institute of Standards and Technology (MNIST) dataset is used to verify the feasibility of the proposed method through experiments. In the test with the MNIST dataset, the reconstruction results demonstrate that in addition to high-precision reconstruction, the proposed FINCH/DLPS method also can effectively retain the 3D information by calibrating the back propagation distance in the case of reducing the complexity of the experiment, further indicating the feasibility and superiority of the proposed FINCH/DLPS method.

Polar optical flow network: extracting overlapped holograms of dynamic particles.

Digital holography with lensless in-line setup has been extensively used in particle field measurements. As particle concentration increases, the holograms of dynamic particles locating at different depths tend to superpose with each other with incoherent overlap, hampering effective measurement of individual particles with incomplete information. Drawing inspiration from suborbicular nature of the in-line holographic fringes, in this study, we propose an optical flow method in polar coordinates to mitigate the overlap issue. The approach employs a radial transformer-enhanced network that leverages both the radial and angular characteristics of the polar hologram. Through ablation tests and experimental results, we have demonstrated the effectiveness and superiority of our proposed method.

Real-time phase imaging with physics-enhanced network and equivariance.

Learning-based phase imaging balances high fidelity and speed. However, supervised training requires unmistakable and large-scale datasets, which are often hard or impossible to obtain. Here, we propose an architecture for real-time phase imaging based on physics-enhanced network and equivariance (PEPI). The measurement consistency and equivariant consistency of physical diffraction images are used to optimize the network parameters and invert the process from a single diffraction pattern. In addition, we propose a regularization method based total variation kernel (TV-K) function constraint to output more texture details and high-frequency information. The results show that PEPI can produce the object phase quickly and accurately, and the proposed learning strategy performs closely to the fully supervised method in the evaluation function. Moreover, the PEPI solution can handle high-frequency details better than the fully supervised method. The reconstruction results validate the robustness and generalization ability of the proposed method. Specially, our results show that PEPI leads to considerable performance improvement on the imaging inverse problem, thereby paving the way for high-precision unsupervised phase imaging.

SERS-based error calibration of a TMB-H2O2 colorimetric system.

3,3',5,5'-tetramethylbenzidine (TMB)-H2O2 is widely used as an effective colorimetric system, in which the color reaction is implemented with peroxidase-catalyzed TMB oxidation by H2O2 that usually measured UV-vis absorption spectra or Raman spectra. However, its low accuracy significantly limits its application. Blue charge transfer complex (CTC), which is the product of TMB and H2O2 reaction and is used as the basis for partial colorimetric methods, usually causes colorimetric error owing to changes in the UV-vis absorption and Raman spectra during TMB oxidation under various environmental conditions (catalyst type, temperature, H2O2 concentration). Herein, we propose a surface-enhanced Raman spectrum (SERS)-based error calibration method to improve the accuracy of the TMB-H2O2 colorimetric system. It is found that under 633 nm laser excitation, TMB has three Raman peaks at 1189, 1335 and 1609 cm-1 in the single-electron oxidation phase, and these peaks disappear completely in the two-electron oxidation phase. By comparing these Raman peaks, we can conveniently obtain the actual process information during TMB oxidation. Using the proposed method, the accuracy of the TMB-H2O2 colorimetric system improved by more than 15%. Importantly, this SERS-based TMB-H2O2 error calibration method will open a new horizon for enzyme-linked immunosorbent assay (ELISA) and other biomedical applications.

Quantitative phase imaging based on model transfer learning.

Convolutional neural networks have been widely used in optical information processing and the generalization ability of the network depends greatly on the scale and diversity of the datasets, however, the acquisition of mass datasets and later annotation have become a common problem that hinders its further progress. In this study, a model transfer-based quantitative phase imaging (QPI) method is proposed, which fine-tunes the network parameters through loading pre-training base model and transfer learning, enable the network with good generalization ability. Most importantly, a feature fusion method based on moment reconstruction is proposed for training dataset generation, which can construct rich enough datasets that can cover most situations and accurately annotated, it fundamentally solves the problem from the scale and representational ability of the datasets. Besides, a feature distribution distance scoring (FDDS) rule is proposed to evaluate the rationality of the constructed datasets. The experimental results show that this method is suitable for different types of samples to achieve fast and high-accuracy phase imaging, which greatly relieves the pressure of data, tagging and generalization ability in the data-driven method.

Diffraction-Net: a robust single-shot holography for multi-distance lensless imaging.

Digital holography based on lensless imaging is a developing method adopted in microscopy and micro-scale measurement. To retrieve complex-amplitude on the sample surface, multiple images are required for common reconstruction methods. A promising single-shot approach points to deep learning, which has been used in lensless imaging but suffering from the unsatisfied generalization ability and stability. Here, we propose and construct a diffraction network (Diff-Net) to connect diffraction images at different distances, which breaks through the limitations of physical devices. The Diff-Net based single-shot holography is robust as there is no practical errors between the multiple images. An iterative complex-amplitude retrieval approach based on light transfer function through the Diff-Net generated multiple images is used for complex-amplitude recovery. This process indicates a hybrid-driven method including both physical model and deep learning, and the experimental results demonstrate that the Diff-Net possesses qualified generalization ability for samples with significantly different morphologies.

Differential phase measurement based on synchronous phase shift determination.

Based on synchronous phase shift determination, we propose a differential phase measurement method for differential interference contrast (DIC) microscopy. An on-line phase shift measurement device is used to generate carrier interferograms and determine the phase shift of DIC images. Then the differential phase can be extracted with the least-squares phase-shifting algorithm. In addition to realizing on-line, dynamic, real-time, synchronous and high precision phase shift measurement, the proposed method also can reconstruct the phase of the specimen by using the phase-integral algorithm. The differential phase measurement method reveals obvious advantages in error compensation, anti-interference, and noise suppression. Both simulation analysis and experimental result demonstrate that using the proposed method, the accuracy of phase shift measurement is higher than 0.007 rad. Very accurate phase reconstructions were obtained with both polystyrene microspheres and human vascular endothelial.

Deep learning-based method for non-uniform motion-induced error reduction in dynamic microscopic 3D shape measurement.

The non-uniform motion-induced error reduction in dynamic fringe projection profilometry is complex and challenging. Recently, deep learning (DL) has been successfully applied to many complex optical problems with strong nonlinearity and exhibits excellent performance. Inspired by this, a deep learning-based method is developed for non-uniform motion-induced error reduction by taking advantage of the powerful ability of nonlinear fitting. First, a specially designed dataset of motion-induced error reduction is generated for network training by incorporating complex nonlinearity. Then, the corresponding DL-based architecture is proposed and it contains two parts: in the first part, a fringe compensation module is developed as network pre-processing to reduce the phase error caused by fringe discontinuity; in the second part, a deep neural network is employed to extract the high-level features of error distribution and establish a pixel-wise hidden nonlinear mapping between the phase with motion-induced error and the ideal one. Both simulations and real experiments demonstrate the feasibility of the proposed method in dynamic macroscopic measurement.

Registration of multi-modal images under a complex background combining multiscale features extraction and semantic segmentation.

Multi-modal imaging technology has a very broad application value in target recognition and other fields, and image registration is one of its key technologies. In this paper, a multi-modal image registration algorithm that combines multiscale features extraction and semantic segmentation is proposed to achieve accurate registration of polarized images and near-infrared images under complex backgrounds. A classical convolutional neural network ResNet is employed to capture the robust feature descriptors, and a convolutional neural network with an attention mechanism is trained to filter out the irrelevant feature points. Further, the two multi-modal images can be further registered. The experimental results show the feasibility and effectiveness of the proposed method.

Hybrid-net: a two-to-one deep learning framework for three-wavelength phase-shifting interferometry.

In this paper, we propose a two-to-one deep learning (DL) framework for three- wavelength phase-shifting interferometry. The interferograms at two different wavelengths are used as the input of the proposed hybrid-net, and the interferogram of the third wavelength is used as the output. Using the advantages of the hybrid learning network, the interferogram of the third wavelength can be obtained accurately. Finally, the three-wavelength phase-shifting interferometry is realized. Compared with the previous DL-based dual-wavelength interferometry (DWI), the proposed method can further improve the measurement range of the sample without changing the DWI system. Especially for the independent step sample, the problem of limited measurement range is solved due to the input of auxiliary information. More importantly, the third wavelength can be set freely according to the measurement requirements, which is no longer limited by the actual laser and can provide more measuring ruler for phase measurement. Both experimental results and simulation analysis demonstrate the proposed method in the feasibility and the performance in improving the measurement range.

Broadband Raman scattering enhancement with reduced heat generation in a dielectric-metal hybrid nanocavity.

The strongly localized electric field achieved in metallic nanoparticles (NPs) and nanostructures are commonly employed to realize surface-enhanced Raman scattering. However, the heat originating from the Ohmic loss of metals may lead to the damage of the analyzed molecules, which severely limits the practical applications of pure-metallic nanostructures. Here, we propose a dielectric-metallic hybrid nanocavity placing silicon (Si) NPs onto a gold (Au) film to realize broadband Raman scattering enhancement with significantly reduced heat generation. Our results reveal that the heat generation is dramatically reduced in the hybrid nanocavity as compared with its pure-metallic counterpart while a significantly enhanced electric field is maintained. We demonstrate numerically and experimentally that the optical resonances, which arise from the coherent coupling of the electric and magnetic dipoles excited inside the Si NP with their mirror images arisen from the Au film, can be employed to enhance the excitation and radiation of Raman signals, respectively. We find that the enhancement in the radiation of Raman signals plays a crucial role in enhancing the total Raman scattering. We also show that the hybrid nanocavity acts as a nano-antenna which effectively radiates Raman signals into the far-field. These findings indicate the advantages of such hybrid nanocavities in temperature-sensitive Raman scattering characterization and supply new strategies for designing nanoscale photonic devices of other functionalities with hybrid nanocavities.

Optical nanoantenna with muitiple surface plasmon resonances for enhancements in near-field intensity and far-field radiation.

Plasmonic nanostructures with dual surface plasmon resonances capable of simultaneously realizing strong light confinement and efficient light radiation are attractive for light-matter interaction and nanoscale optical detection. Here, we propose an optical nanoantenna by adding gold nanoring to the conventional Fano-type resonance antenna. With the help of gold nanoring, the following improvements are simultaneously realized: (1). The near-field intensity of the Fano-type antenna is further enhanced by the Fabry Perot-like resonance formed by the combination of the gold nanoring and the substrate waveguide layer. (2). Directional radiation is realized by the collaboration of the gold nanoring and the Fano-type antenna, thus improving the collection efficiency of the far-field signal. (3). The multi-wavelength tunable performance of the Fano resonance antenna is significantly improved by replacing the superradiation mode in the Fano resonance with the dipole resonance induced by the gold nanoring. The optical properties of the nanoantennas are demonstrated by numerical simulations and practical devices. Therefore, the proposed optical nanoantenna provides a new idea for further improving the performance of conventional Fano-type nanoantennas and opens new horizons for designing plasmonic devices with enhancements in both near- and far-field functionalities, which can be applied in a wide range of applications such as surface-enhanced spectroscopy, photoluminescence, nonlinear nanomaterials/emitters and biomedicine sensing.

Au/Ag composite-based SERS nanoprobe of Cr3.

Quantitative characterization of Cr3+, an important element revealing human metabolism and biological environmental variation, is still difficult to achieve by conventional biochemical methods due to the lack of high-sensitivity, real-time techniques with rapid response detection. Using surface-enhanced Raman scattering (SERS), we construct an Au/Ag composite-based SERS nanoprobe for the quantitative characterization of Cr3+ content in solution, in which DL-mercaptosuccinic acid (DL-MSA) is employed for Raman signal enhancement, and 4-mercaptobenzoic acid (4-MBA) is chosen as the Raman reporter. The achieved result demonstrates obvious advantages of the synthesized Au/Ag composite-based SERS nanoprobe in sensitivity and response speed. Importantly, this Au/Ag composite-based SERS nanoprobe might provide a new strategy for dynamic monitoring of Cr3+ content in human metabolism.

Comparison Between SonoVue and Sonazoid Contrast-Enhanced Ultrasound in Characterization of Focal Nodular Hyperplasia Smaller Than 3 cm.

OBJECTIVES: This study aimed to compare the diagnostic efficacy of contrast-enhanced ultrasound (CEUS), including SonoVue (SV; sulfur hexafluoride; Bracco SpA, Milan, Italy) and Sonazoid (SZ; perflubutane; GE Healthcare, Oslo, Norway), and explore the differences between them in the characterization of CEUS features in focal nodular hyperplasia (FNH) smaller than 3 cm. METHODS: This retrospective study included 31 lesions smaller than 3 cm diagnosed as FNH by CEUS between April 2019 and November 2019. Nine patients underwent SZ CEUS examinations, and 22 patients underwent SV CEUS examinations; all of them were confirmed by pathologic examinations or 2 other kinds of CEUS methods. We compared the CEUS features between SZ and SV in different phases, including arterial, portal venous, delayed, and Kupffer (SZ) phases. RESULTS: Twenty-eight lesions were eventually diagnosed as FNH; 3 were misdiagnosed as FNH by SV CEUS. The overall diagnostic accuracy of CEUS including SZ and SV was 90.3% (28 of 31). No significant difference was found (P > .05) for the positive predictive value. Likewise, no significant difference in depicting centrifugal filling (9 of 9 versus 19 of 19), spoke wheel artery (6 of 9 versus 8 of 19), or feeding artery (2 of 9 versus 10 of 19) features was found between the contrast agents; However, SZ was significantly better at depicting the presence of a central scar than SV (5 of 9 versus 3 of 19; P = .030). Misdiagnosed cases are discussed in detail. CONCLUSIONS: Contrast-enhanced ultrasound enables an accurate diagnosis in FNH smaller than 3 cm. Sonazoid CEUS and SV CEUS were comparable in diagnosing small FNH, and both agents were highly capable of depicting the centrifugal filling dynamic process of FNH smaller than 3 cm. Sonazoid CEUS might be better than SV CEUS at depicting a central scar.

Quantitative phase imaging in dual-wavelength interferometry using a single wavelength illumination and deep learning.

In this manuscript, we propose a quantitative phase imaging method based on deep learning, using a single wavelength illumination to realize dual-wavelength phase-shifting phase recovery. By using the conditional generative adversarial network (CGAN), from one interferogram recorded at a single wavelength, we obtain interferograms at other wavelengths, the corresponding wrapped phases and then the phases at synthetic wavelengths. The feasibility of the proposed method is verified by simulation and experiments. The results demonstrate that the measurement range of single-wavelength interferometry (SWI) is improved by keeping a simple setup, avoiding the difficulty caused by using two wavelengths simultaneously. This will provide an effective solution for the problem of phase unwrapping and the measurement range limitation in phase-shifting interferometry.

Image definition assessment based on Tchebichef moments for micro-imaging.

This paper proposes a Tchebichef moment (TM)-based image definition assessment (IDA) method that employs the difference in the logarithmic spectra (DLS). To avoid the influence of the original image, the essential element point spread function (PSF) is extracted from the DLS to characterize the IDA function uniquely. The amplification of the PSF spot radius to the defocus amount in the micro-imaging system enhances the featural differences among the DLSs, thereby improving the sensitivity to the defocus amount. The DLS with an obvious geometric feature variation is described by a TM with a low order, which improves the anti-noise performance. The performed simulation and experiment verified the superiority of the proposed method.