Image Research Articles

A water-soluble aggregation-induced emission luminogen for NIR-I/NIR-II fluorescence imaging of breast cancer bone metastases

Advanced breast cancer is prone to bone metastasis, which is the most common bone metastatic tumor. Current clinical methods for diagnosing breast cancer bone metastases rely on serological markers, computed tomography and magnetic resonance imaging. However, these technologies cannot meet patients' needs due to the delayed screening, complex procedures and expensive equipment. Optical imaging currently exhibits inexhaustible and vigorous vitality in the field of diagnosis thanks to its advantages of simplicity, good controllability and high resolution. Nevertheless, the development of prominent chromophores for the diagnosis of breast cancer bone metastases is an appealing yet significantly challenging task. In this contribution, we rationally designed and synthesized three water-soluble aggregation-induced emission (AIE) luminogens, named PEGTPA-BTD, PEGTPA-NTD, and PEGTPA-NSD, by introducing different moieties as electron acceptors and PEGylated triphenylamine derivatives as electron donors and hydrophilic moieties. In vitro experiments showed that PEGTPA-NSD has a longer absorption and emission wavelength, where the emission wavelength can extend into NIR-II region. Besides, PEGTPA-NSD could self-assemble into stable nanoparticles in aqueous solution. Cell experiments showed that PEGTPA-NSD had no obvious dark toxicity to tumor cells or normal cells, and were easily taken in by tumor cells for cell imaging. What's more, PEGTPA-NSD NPs possessed excellent fluorescence imaging performance and biocompatibility in vivo for breast cancer bone metastases in NIR-I and NIR-II region, respectively. In summary, PEGTPA-NSD is the first reported aggregation-induced emission luminogens (AIEgens) that can self-assemble to nanoparticles in aqueous solution for NIR-I/NIR-II fluorescence imaging of breast cancer bone metastases. These findings would provide new strategies for optical diagnostic imaging of breast cancer bone metastases to better advance clinical technology development.

A soft scanning electron microscopy for efficient segmentation of alloy microstructures based on a new self-supervised pre-training deep learning network

To provide an on-site metallographic segmentation using only optical microscopy images, sSEM-Net, a soft scanning electron microscopy network, is developed based on a self-supervised pre-training deep learning framework. During model training, only a sparse collection of SEM images is necessary for annotation assistance. By integrating CNN and Transformer, sSEM-Net efficiently utilizes global context information while mitigating data dependency and computational resource constraints. Using only readily available optical microscopy images as input, sSEM-Net achieves metallographic segmentation comparable to SEM images, catering to rapid and cost-effective industrial needs. This methodology leverages non-destructive inspection attributes, catering to rapid and cost-sensitive industrial requirements. The efficacy of the proposed sSEM-Net is demonstrated through metallographic structure analysis of TC4 titanium alloy, with potential extensions to other alloy types.

Cr3+-doped Na2CaSn2Ge3O12 garnet showing broadband near-infrared emission at 820 nm with zero thermal quenching and near-unity quantum yield

The continually advancing near-infrared (NIR) phosphor-converted light-emitting diode (pc-LED) hold pivotal significance in optical imaging and NIR spectroscopy. Cr3+-doped garnet broadband NIR phosphors have attracted considerable attention owing to their high optical performance enabled by the rigid structure. Nonetheless, the emission wavelength is severely restricted, and longer wavelength emissions usually result in inferior quantum efficiency and poor luminescence stability. In this work, the concurrent reconstruction of dodecahedral and octahedral sites by introducing [Na+-Sn4+]C unit in the framework of Na2CaSn2Ge3O12 garnet contributes to the Cr3+ broadband NIR emission spanning 700–1200 nm, with a peak at 820 nm. Utilizing a charge compensation strategy via Ta5+ codoping largely boosts the internal quantum efficiency (IQE) of Na2CaSn2Ge3O12:Cr3+ from 93.06 % up to 95.04 %. Trap-mediated energy compensation is proposed to achieve a record-breaking zero-thermal-quenching behavior (99.39 %@423 K) of Cr3+-doped garnet with such a longer wavelength emission. The encapsulated prototype broadband NIR pc–LED gives rise to an overwhelming NIR output power of 590.9 mW@1300 mA and a photoconversion efficiency (PCE) of 32.62 % at 30 mA, rendering exceptional performance in night vision, detection, and NIR imaging applications.

Impact of Graphene Nanoparticles on DLP-Printed Parts' Mechanical Behavior

Digital Light Processing (DLP) is one of the most promising techniques among the additive manufacturing (AM) technologies for polymer resin. The polymer parts produced through this technique demonstrate a diverse range of characteristics that can be specifically designed for various fields of application. Specific attributes can be attained by utilizing polymer composites composed of multiple materials in numerous ratios. This research delves into evaluating and comparing different properties, including microstructure, surface texture, and mechanical behavior, of resin-based polymer composites fabricated using the DLP 3D printing technology. To achieve this, specimens have been printed using photopolymer resin as the base material, with varying percentages of graphene nanoparticles added to the resin. Tensile tests and particle analysis based on optical microscope images validate that optimizing parameters, especially the energy setting of the printer, significantly impact the printed samples' strength, surface texture, layering, and microstructure. The findings indicate that at a specific percentage of graphene, such as 0.5%, there is an increase in tensile strength by 38.1%, Young’s modulus by 54.7%, and Yield strength by 11.2%, accompanied by an improved surface roughness. A graphene concentration of 0.75% results in diminished tensile strength, yield strength, and Young’s modulus. The significance of fine-tuning printing parameters to achieve desired properties in resin-based polymer composites manufactured via 3D printing is highlighted.

A Multi-aperture Coaxial Projector Balancing Shadow Suppression and Deblurring.

This paper proposes a projection system that optically removes the cast shadow in projection mapping. Specifically, we realize the large-aperture (LA) projection using a large-format Fresnel lens to suppress cast shadows by condensing the projection light from a wide viewing angle. However, the resolution and contrast of the projected results are significantly degraded by defocus blur, veiling glare, and stray light caused by the aberration of an LA Fresnel lens. To solve the technical problems, we employ two different approaches: optical and digital image processing methods. First, we introduce a residual projector with a typical aperture lens on the same optical axis as the LA projector, projecting the residual (i.e., high-frequency) components attenuated in the LA projection. These projectors play different roles in shadow suppression and blur compensation, both achieved by projecting simultaneously. Secondly, we optimize the pair of projection images that can balance the shadow suppression and deblurring performance of our projection system. We implemented a proof-of-concept prototype and validated the above-mentioned techniques through projection experiments and a user study.

Semantic segmentation of optical satellite images for the illegal construction detection using transfer learning

As urban populations grow, the demand for new infrastructure intensifies. However, some builders often neglect to obtain necessary permits, resulting in unstable structures that endanger public safety and disrupt city planning. Illegal construction poses significant challenges to economic development and social harmony. To tackle this issue, there is an urgent need for an automated illegal building monitoring system that can accurately identify and notify authorities about unlawful constructions. Current methods are often inefficient due to limited data access, inadequate analysis, and lack of automation.Recent technological advancements suggest that using satellite imagery alongside semantic segmentation is a highly effective approach for detecting illegal buildings. This process not only ensures the safety and compliance of structures but also enhances overall regulatory enforcement. This research distinguishes itself from previous studies by primarily focusing on illegal construction. It employs a diverse range of machine learning models, including the U-Shaped Network and Visual Geometry Group, and incorporates customized evaluation metrics that are not typically found in earlier research. Additionally, it utilizes real-world data, enhancing its practical relevance, and features IoT integration for real-time monitoring capabilities.We chose U-Net as our architecture due to its symmetrical design, which facilitates effective feature extraction. Its skip connections play a crucial role in preserving important features during the segmentation process. U-Net is known for its high accuracy and adaptability across various domains, making it versatile for our needs. Additionally, it performs well even with limited data, demonstrating robustness in diverse conditions. Furthermore, its compatibility with transfer learning enhances its applicability. For all these reasons, U-Net was selected as our model.Our experiments revealed that the Unet_mini model outperformed all others, demonstrating its effectiveness in addressing illegal construction challenges in urban areas. Qualitative results further illustrate the model's robustness, showing a significant reduction in false positives and improved accuracy in identifying illegal structures. This comprehensive approach not only improves detection rates but also contributes to more efficient regulatory enforcement, thereby promoting safer urban environments.This research distinguishes itself from previous studies by primarily focusing on illegal construction detection rather than general segmentation tasks. It employs a diverse range of machine learning models, including the U-Shaped Network and Visual Geometry Group, and integrates customized evaluation metrics that are not typically found in earlier research. Furthermore, it utilizes real-world data, enhancing its practical relevance, and incorporates IoT integration for real-time monitoring capabilities, setting it apart from existing studies.

The GROND gamma-ray burst sample

A dedicated gamma-ray burst (GRB) afterglow observing program was performed between 2007 and 2016 with GROND, a seven-channel optical and near-infrared imager at the 2.2m telescope of the Max-Planck Society at ESO/La Silla, In this first of a series of papers, we describe the GRB observing plan, providing first readings of all so far unpublished GRB afterglow measurements and some observing statistics. In total, we observed 514 GRBs with GROND, including 434 Swift-detected GRBs, representing 81% of the observable Swift sample. For GROND-observations within 30 min of the GRB trigger, the optical/NIR afterglow detection rate is 81% for long- and 57% for short-duration GRBs. We report the discovery of ten new GRB afterglows plus one candidate, along with redshift estimates (partly improved) for four GRBs and new host detections for seven GRBs. We identify the (already known) afterglow of GRB 140209A as the sixth GRB exhibiting a 2175 Å dust feature. As a side result, we identified two blazars, with one at a redshift of z = 3.8 (in the GRB 131209A field).

Correlation Between Photoreceptor and Vascular Parameters in Diabetic Retinopathy Using Adaptive Optics.

This study aimed to investigate correlations between photoreceptor and vascular parameters in varying stages of diabetic retinopathy (DR) using adaptive optics (AO) imaging. In this single-center, prospective cohort study, 29 participants (46 eyes) were classified into control/mild non-proliferative DR (NPDR), moderate/severe NPDR, and proliferative DR. AO images of photoreceptors and retinal vasculature were analyzed, and Spearman's correlation (ρ) was used to assess relationships between photoreceptor density and vascular parameters. Higher cone density was inversely associated with total vessel (ρ = 0.22, P = 0.03) and lumen diameters (ρ = -0.24, P = 0.01), while higher dispersion was associated with total vessel (ρ = 0.19, P = 0.06) and lumen diameters (ρ = 0.21, P = 0.04). These associations were primarily significant in mild NPDR. No significant correlations were found in advanced DR stages. This study underscores intricate neurovascular correlations in early-stage DR, suggesting these parameters may aid in early disease detection. Further research is needed to understand whether similar correlations exist in advanced DR. [Ophthalmic Surg Lasers Imaging Retina 2024;55:XX-XX.].

Design of eye phantom for optical coherence tomography angiography study.

Tissue phantoms play an important role in validating biomedical imaging techniques. For optical imaging systems such as optical coherence tomography, creating layers of phantoms with different refractive indices is important. This paper explored various phantom materials to create eye-mimicking phantoms. The phantom also included microfluidic channels to mimic vasculature. To verify the optical properties of the created phantom, the transmission and reflection spectroscopy was performed using a photoluminescent spectrometer studying the complete visible and near infrared spectrum. We created a 3D model to house multiple layers that mimicked the retina at one end and the contact lens at the other, representing the anterior segment of the eye. A realistic retinal layer-mimicking, which, among other things, may pave the way for new combined imaging modalities.

Enhanced heavy oil fouling resistance of cobalt-based spray-deposited coatings by alloy design and heat treatment

Heavy oil hydrocracking fouls critical reactor components, causing premature turnarounds. Developing materials with superior resistance to coking, sulfidation, and wear proves crucial. High-velocity oxy-fuel (HVOF) coating improves materials' properties as an industrial-ready solution. This study explores the effect of alloying elements on the HVOF-sprayed CoCrMoSi coating model and their role in protecting against heavy oil fouling. Heat treatment reduced the coatings' inhomogeneity and microstructural defects, substantially improving their protection against fouling. A novel optical image treatment has been developed to semi-quantitatively assess the surface fouling intensity. The study provides insights into the complex interplay between alloy composition, microstructure, heat treatment, and surface energy in optimizing coating protection against heavy oil fouling.

Simultaneous X-Ray and Optical Polarization Observations of the Blazar Mrk 421

We present near-simultaneous X-ray and optical polarization measurements in the high synchrotron peaked (HSP) blazar Mrk 421. The X-ray polarimetric observations were carried out using Imaging X-ray Polarimetry Explorer (IXPE) on 2023 December 6. During IXPE observations, we also carried out optical polarimetric observations using 104 cm Sampurnanand telescope at Nainital and multiband optical imaging observations using 2 m Himalayan Chandra Telescope at Hanle. From model-independent analysis of IXPE data, we detected X-ray polarization with degree of polarization (ΠX) of 8.5% ± 0.5% and an electric vector position angle (ΨX) of 10.°6 ± 1.°7 in the 2−8 keV band. From optical polarimetry on 2023 December 6, in B, V, and R bands, we found values of Π B = 4.27% ± 0.32%, Π V = 3.57% ± 0.31%, and Π R = 3.13% ± 0.25%. The value of Π B is greater than that observed at longer optical wavelengths, with the degree of polarization suggesting an energy-dependent trend, gradually decreasing from higher to lower energies. This is consistent with that seen in other HSP blazars and favors a stratified emission region encompassing a shock front. The emission happening in the vicinity of the shock front will be more polarized due to the ordered magnetic field resulting from shock compression. The X-ray emission, involving high-energy electrons, originates closer to the shock front than the optical emission. The difference in the spatial extension could plausibly account for the observed variation in polarization between X-ray and optical wavelengths. This hypothesis is further supported by the broadband spectral energy distribution modeling of the X-ray and optical data.

Metasurface-enabled 3D imaging via local bright spot gray scale matching using the structured light dot array.

Three-dimensional (3D) imaging is widely utilized in various applications, such as light detection, autonomous vehicles, and machine vision. However, conventional 3D imaging systems often rely on bulky optical components. Metasurfaces, as next-generation optical devices, possess flexible wavefront modulation capabilities and excellent combination with computer vision algorithms. Here, we propose a large field-of-view (FOV) structured light dot array projection device based on a metasurface, covering a 2π-FOV, for projecting coded point clouds in Fourier space. We explore a local bright spot gray scale matching algorithm for depth extraction, enabling 3D imaging. This algorithm simplifies the data processing flow and optimizes depth extraction and feature matching processes through a customized region gray scale comparison. As a result, it effectively reduces computational complexity and enhances tolerance to image quality fluctuations. The proposed approach provides new possibilities for developing compact and high-performance planar 3D optical imaging devices, which will drive the advancement of fields such as computer vision and artificial intelligence.

Synthesis and evaluation of a novel BODIPY fluorescent probe targeting integrin αvβ3 for cancer diagnosis.

A series of integrin αvβ3 targeting BODIPY-RGD conjugate were designed and synthesized. Their in vitro and in vivo fluorescence imaging behaviors were investigated. The small molecule compound was designed as an optical imaging near-infrared fluorescent dye which was combined RGD peptide with the meso-amide BODIPYs using succinic moiety as a spacer. The construction alleviated the traditional BODIPY problems including poor water solubility, aggregate caused quench (ACQ) effect, low biocompatibility, etc. In cellular research, BDP-RGD-2 showed rapid, selective uptake in 3 highly expressing integrin αvβ3 cell lines MDA-MB-231, A549, U87MG at different extent rather than an integrin αvβ3 low level expression cell MCF-7. In animal study, fluorescence imaging of U87MG model targeted by BDP-RGD-2 displayed a highest tumor uptake level and T/N ratio up to 6h after tail-intravenous injection, which demonstrated BDP-RGD-2 was a promising probe for tracing integrin αvβ3 overexpressing tumors.

Intelligent Sentinel satellite image processing technology for land cover mapping

Purpose. This article proposes to develop an intelligent Sentinel satellite image processing technology for land cover mapping using convolutional neural networks. The result will be an image with improved spatial resolution. Methodology. The paper presents a technology using a combination of biquadratic interpolation, histogram alignment, PCA transform, as well as a parallel residual architecture of convolutional neural networks. The technology increases the information content of Sentinel-2 optical images by combining 10 and 20-meter resolution data, resulting in primary 20-meter images with improved spatial resolution. Findings. The root mean square error (RMSE = 3.64) indicates a high accuracy in reproducing the spectral properties of the images. The correlation coefficient (CC = 0.997) confirms a high linear relationship between the estimated and observed images. The low value of Spectral Angle Mapper (SAM = 0.52) with the high Universal Image Quality Index (UIQI = 0.999) indicates high quality and structural similarity between the synthesized and reference images. These results confirm the proposed technology’s effectiveness in enhancing the spatial resolution of Sentinel satellite images. Originality. Traditional pansharpening methods of multispectral images developed for satellite images with panchromatic channels cannot be directly applied to Sentinel multispectral data, because these images do not contain a panchromatic channel. In addition, atmospheric conditions and the presence of clouds affect the quality of optical images, complicating their further thematic processing. The proposed technology, using biquadratic interpolation, histogram alignment, convolutional neural networks, and PCA transformation, removes clouds and enhances the spatial resolution of the primary 20-meter optical satellite image channels of Sentinel-2. This technology reduces color distortion and increases the detail of digital optical images, which allows for more accurate analysis of the state of the earth’s surface. Practical value. The results obtained can be used to improve the methods for processing Sentinel satellite images, which provide high spatial resolution and accurate preservation of spectral characteristics. It provides the foundation for the development of new geographic information systems for land cover monitoring.

Removing cloudiness on optical space images by a generative adversarial network model using SAR images

The object of this study is the process of removing cloudiness on optical space images. Solving the cloudiness removal task is an important stage in processing data from the Earth remote probing (ERP) aimed at reconstructing the information hidden by these atmospheric disturbances. The analyzed shortcomings in the fusion of purely optical data led to the conclusion that the best solution to the cloudiness removal problem is a combination of optical and radar data. Compared to conventional methods of image processing, neural networks could provide more efficient and better performance indicators due to the ability to adapt to different conditions and types of images. As a result, a generative adversarial network (GAN) model with cyclic-sequential 7-ResNeXt block architecture was constructed for cloud removal in optical space imagery using synthetic aperture radar (SAR) imagery. The model built generates fewer artifacts when transforming the image compared to other models that process multi-temporal images. The experimental results on the SEN12MS-CR data set demonstrate the ability of the constructed model to remove dense clouds from simultaneous Sentinel-2 space images. This is confirmed by the pixel reconstruction of all multispectral channels with an average RMSE value of 2.4 %. To increase the informativeness of the neural network during model training, a SAR image with a C-band signal is used, which has a longer wavelength and thereby provides medium-resolution data about the geometric structure of the Earth's surface. Applying this model could make it possible to improve the situational awareness at all levels of control over the Armed Forces (AF) of Ukraine through the use of current space observations of the Earth from various ERP systems

Investigation of the Mechanical Properties of Reinforced Calcareous Sand Using a Permeable Polyurethane Polymer Adhesive.

Calcareous sand has been widely used as a construction material for offshore projects; however, the problem of foundation settlement caused by particle crushing cannot be ignored. Although many methods for reinforcing calcareous sands have been proposed, they are difficult to apply on-site. In this study, a permeable polyurethane polymer adhesive (PPA) was used to reinforce calcareous sands, and its mechanical properties after reinforcement were investigated through compression creep, direct shear, and triaxial shear tests. The reinforcement mechanism was analyzed using optical microscopy, CT tomography, and mercury intrusion porosimetry. The experimental results indicate that there is a critical time during the compression creep process. Once the critical time is surpassed, creep accelerates again, causing failure of the traditional Burgers and Murayama models. The direct shear strength of the fiber- and geogrid-reinforced calcareous sand reinforced by PPA was approximately nine times greater than that without PPA. The influence of normal stress was not significant when the moisture content was less than 10%, but when the moisture content was more than 10%, the shear strength increased with an increase in vertical normal stress. Strain-softening features can be observed in triaxial shear tests under conditions of low confining pressure, and the relationship between the deviatoric stress and strain can be described using the Duncan-Chang model before softening occurs. The moisture content also has a significant influence on the peak strength and cohesive force but has little influence on the internal friction angle and Poisson's ratio. This influence is caused by the different PPA structures among the particles. The higher the moisture content, the greater the number of pores left after grouting PPA.

Identifying retinal pigment epithelium cells in adaptive optics–optical coherence tomography images with partial annotations and superhuman accuracy

Identifying retinal pigment epithelium cells in adaptive optics–optical coherence tomography images with partial annotations and superhuman accuracy

All black: a microplastic extraction combined with colour-based analysis allows identification and characterisation of tire wear particles (TWP) in soils

While tire wear particles (TWP) have been estimated to represent more than 90% of the total microplastic (MP) emitted in European countries and may have environmental health effects, only few data about TWP concentrations and characteristics are available today. The lack of data stems from the fact that no standardized, cost efficient or accessible extraction and identification method is available yet. We present a method allowing the extraction of TWP from soil, performing analysis with a conventional optical microscope and a machine learning approach to identify TWP in soil based on their colour. The lowest size of TWP which could be measured reliably with an acceptable recovery using our experimental set-up was 35 µm. Further improvements would be possible given more advanced technical infrastructure (higher optical magnification and image quality). Our method showed a mean recovery of 85% in the 35–2000 µm particle size range and no blank contamination. We tested for possible interference from charcoal (as another black soil component with similar properties) in the soils and found a reduction of the interference from charcoal by 92% during extraction. We applied our method to a highway adjacent soil at 1 m, 2 m, 5 m, and 10 m and detected TWP in all samples with a tendency to higher concentrations at 1 m and 2 m from the road compared to 10 m from the road. The observed TWP concentrations were in the same order of magnitude as what was previously reported in literature in highway adjacent soils. These results demonstrate the potential of the method to provide quantitative data on the occurrence and characteristics of TWP in the environment. The method can be easily implemented in many labs, and help to address our knowledge gap regarding TWP concentrations in soils.

Stent Graft Restenosis: Insights From Optical Coherence Tomography Imaging and Treatment Considerations

Stent Graft Restenosis: Insights From Optical Coherence Tomography Imaging and Treatment Considerations

Prediction of fellow eye neovascularization in type 3 macular neovascularization (Retinal angiomatous proliferation) using deep learning.

To establish a deep learning artificial intelligence model to predict the risk of long-term fellow eye neovascularization in unilateral type 3 macular neovascularization (MNV). This retrospective study included 217 patients (199 in the training/validation of the AI model and 18 in the testing set) with a diagnosis of unilateral type 3 MNV. The purpose of the AI model was to predict fellow eye neovascularization within 24 months after the initial diagnosis. The data used to train the AI model included a baseline fundus image and horizontal/vertical cross-hair scan optical coherence tomography images in the fellow eye. The neural network of this study for AI-learning was based on the visual geometry group with modification. The precision, recall, accuracy, and the area under the curve values of receiver operating characteristics (AUCROC) were calculated for the AI model. The accuracy of an experienced (examiner 1) and less experienced (examiner 2) human examiner was also evaluated. The incidence of fellow eye neovascularization over 24 months was 28.6% in the training/validation set and 38.9% in the testing set (P = 0.361). In the AI model, precision was 0.562, recall was 0.714, accuracy was 0.667, and the AUCROC was 0.675. The sensitivity, specificity, and accuracy were 0.429, 0.727, and 0.611, respectively, for examiner 1, and 0.143, 0.636, and 0.444, respectively, for examiner 2. This is the first AI study focusing on the clinical course of type 3 MNV. While our AI model exhibited accuracy comparable to that of human examiners, overall accuracy was not high. This may partly be a result of the relatively small number of patients used for AI training, suggesting the need for future multi-center studies to improve the accuracy of the model.