Image Research Articles

Effects of nonmigrating diurnal tides on the Na layer in the mesosphere and lower thermosphere

Abstract. The influence of nonmigrating diurnal tides on Na layer variability in the mesosphere and lower thermosphere regions is investigated for the first time using data from the Optical Spectrograph and InfraRed Imaging System (OSIRIS) on the Odin satellite and Specified Dynamics Whole Atmosphere Community Climate Model (SD-WACCM) with metal chemistry. The Na density from OSIRIS exhibits a clear longitudinal variation indicative of the presence of tidal components. Similar variability is seen in the SD-WACCM result. Analysis shows a significant relationship between the nonmigrating diurnal tides in Na density and the corresponding temperature tidal signal. Below 90 km, the three nonmigrating diurnal tidal components in Na density show a significant positive correlation with the temperature tides. Conversely, the phase mainly indicates a negative correlation above 95 km. Around the metal layer peak, the response of the Na density to a 1 K change in tidal temperature is estimated to be 120 cm−3.

High-Resolution SAR-to-Multispectral Image Translation Based on S2MS-GAN

Synthetic aperture radar (SAR) has been extensively applied in remote sensing applications. Nevertheless, it is a challenge to process and interpret SAR images. The key to interpreting SAR images lies in transforming them into other forms of remote sensing images to extract valuable hidden remote sensing information. Currently, the conversion of SAR images to optical images produces low-quality results and incomplete spectral information. To address these problems, an end-to-end network model, S2MS-GAN, is proposed for converting SAR images into multispectral images. In this process, to tackle the issues of noise and image generation quality, a TV-BM3D module is introduced into the generator model. Through TV regularization, block-matching, and 3D filtering, these two modules can preserve the edges and reduce the speckle noise in SAR images. In addition, spectral attention is added to improve the spectral features of the generated MS images. Furthermore, we construct a very high-resolution SAR-to-MS image dataset, S2MS-HR, with a spatial resolution of 0.3 m, which is currently the most comprehensive dataset available for high-resolution SAR-to-MS image interpretation. Finally, a series of experiments are conducted on the relevant dataset. Both quantitative and qualitative evaluations demonstrate that our method outperforms several state-of-the-art models in translation performance. The solution effectively facilitates high-quality transitions of SAR images across different types.

Development of Flexible Hydrophobic Polypropylene/SPCB/GNP Hybrid Nanocomposite Films With Excellent EMI Shielding and Mechanical Properties

ABSTRACTThe demand for effective electromagnetic interference (EMI) shielding materials has driven the development of novel and facile approach for fabrication of conductive polymer nanocomposites. In this study, a polypropylene (PP)‐based hybrid nanocomposite was prepared by incorporating Super P conductive carbon black (SPCB) and graphene nanoplatelets (GNP) via latex technology. A simplistic method of fabrication was adopted where the nanofillers were stirred into the PP latex at room temperature, ensuring the proper dispersion of fillers within the matrix. Hybridizing the nanocomposite by the usage of two different fillers has resulted in a substantial improvement in EMI shielding performance of the material along with its mechanical and thermal properties. The samples were fabricated and tested in three different thicknesses to ensure that EMI shielding effectiveness (EMI SE) is a thickness dependent property and also the studies were carried out over a broad range of microwave frequency. A high electrical conductivity of 7.6 S m−1 was obtained and exceptional EMI SE of 65 dB (X band), 69.76 dB (Ku band), and 72.38 dB (K band) was showcased by 2 mm thickness composites. The optical images of PP latex incorporated with the fillers gave a clear understanding about the formation of conducting networks within the polymer matrix at percolation threshold. The addition of SPCB and GNP also showed a significant increase in hardness and tensile strength of the composite and helped in protecting the polymer matrix against photo radiation. The fillers also aided in the enhancement of the hydrophobicity of the films. The thermal stability, crystallization temperature (TC) and melting temperature (TM) of the composites were also improved when compared to that of neat The works aims at fabricating a hydrophobic and low‐density polymer nanocomposite showing excellent absorption dominant EMI SE.

Magnetoplasmonic Triblock Nanorods for Collective Linear Dichroism.

Polarized light detection is crucial for advancements in optical imaging, positioning, and obstacle avoidance systems. While optical nanomaterials sensitive to polarization are well-established, the ability to align these materials remains a significant challenge. Here, we introduce Au-Fe3O4-Au triblock nanorods as a novel solution. Synthesized via a space-confined seeded growth method, these magnetoplasmonic nanocomposites uniquely combine the strong polarization capabilities of Au nanorods with the magnetic alignment properties of Fe3O4 nanorods. This architecture results in exceptional collective linear dichroism, achieving a polarization ratio of approximately 14 at the device level. Our nanorods exhibit high detection sensitivity and laser damage resistance, positioning them as a promising platform for developing advanced optical devices.

Automated segmentation of retinal layers in optical coherence tomography images using Xception70 feature extraction

Optical coherence tomography (OCT) imaging plays a critical role in evaluating retinal layer thickness, serving as a pivotal diagnostic tool for numerous retinal conditions. However, challenges such as speckle noise, poor image contrast, and ambiguous retinal detachments, like drusen, often hinder accurate segmentation of retinal layers. To address these challenges and enhance diagnostic precision, we introduce the Retinal Segmentation Network, or "Ret-Seg Net," a deep neural network-based approach. Leveraging the advanced Xception70 feature extractor, Ret-Seg Net extracts and comprehends the intricate properties of retinal layers. By integrating acquired feature maps from Xception70 into the atrous spatial pyramid-pooling module, Ret-Seg Net extracts multiscale feature information. The encoder-decoder module of Ret-Seg Net achieves automated segmentation of retinal layers in OCT images by reconstructing distinct retinal layer borders. This advanced module accurately recognizes and differentiates retinal layer boundaries, providing precise and reliable segmentation. Validation of our approach using real-time images and the Duke dataset, comprising 310 volumes with 40 B-scans each, demonstrates outstanding performance. Mean intersection over union (MIoU) and sensitivity (Se) metrics achieved remarkable values of 94.52 % and 96.25 % respectively. Furthermore, our approach offers a versatile segmentation framework applicable to various tissues and cell types in clinical settings. Automating segmentation of retinal layers enhances precision in disease identification and monitoring while significantly improving labor efficiency by reducing the need for manual segmentation.

Long-term changes of sodium column abundance at 24.6°S above the Atacama Desert in Chile

Aims. The utilisation of artificial laser guide star (LGS) obviates the necessity for a prominent natural guide star (NGS) within adaptive optics (AO) systems. High-power lasers are fundamental components of most AO systems today. The generation of an LGS relies on the excitation of sodium (denoted by its symbol Na) atoms situated in the upper atmosphere. Therefore, the sodium vertical column density (denoted as CNa) is a crucial parameter. Beyond ensuring the optimal and stable performance of an AO system, knowledge of the return flux from an LGS is imperative during the design phase, aiding in the accurate specification of both the LGS and the AO system. The availability of sodium in the upper atmosphere has been the focal point of diverse studies, exhibiting a pronounced dependence on the specific observatory site. Furthermore, it is well established that CNa varies across multiple timescales, including hours, nights, months, seasons, and even several years. As many of the world’s largest telescopes are located in the Atacama Desert in northern Chile, our objective is to provide CNa statistics pertinent to this specific region. Methods. We used telemetry data from the AO systems operational at the Paranal Observatory (24.6°S, 70.4°W): Ground Atmospheric Layer Adaptive Corrector for Spectroscopic Imaging (GALACSI) and Ground layer Adaptive Optics system Assisted by Lasers (GRAAL). We combined these data with measurements from two space instruments: SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) and Optical Spectrograph and Infrared Imaging System (OSIRIS), as well as with simulated data from the Whole Atmosphere Community Climate Model (WACCM). We carefully analysed and compared these datasets to develop a statistical model for the temporal variations of CNa. Results. We validated the use of the AO telemetry data from Paranal systems to retrieve the CNa. The near-continuous measurements encompassing the period from mid-2017 to the end of 2023 facilitated the determination of monthly and yearly abundance and variability of Na in the mesopause region. Throughout the complete years of measurement, the annual and semi-annual variations exhibit consistent characteristics that align with previously documented findings in atmospheric studies. Through meticulous comparison and the fitting of various long-term datasets, we formulated a model depicting the evolution of CNa over time. The validity of our data processing and model is scrutinised, and the results obtained for the Paranal latitude exhibit noteworthy concordance with the findings of other studies.

Strength in Numbers: A Giant NIR‐II AIEgen with One‐for‐All Phototheranostic Features for Exceptional Orthotopic Bladder Cancer Treatment

One‐for‐all phototheranostics that allows the simultaneous implementations of multiple optical imaging and therapeutic modalities by utilizing a single component, is growing into a sparkling frontier in cancer treatment. Of particular interest is phototheranostic agent with emission in the second near‐infrared (NIR‐II) window. Nevertheless, the practical uses of those conventional NIR‐II agents are severely impeded by their unsatisfactory features including insufficient stability, low synthetic yield, to be extended absorption/ emission wavelengths, and inefficient phototheranostic outcomes. Developing exceptional phototheranostic agents is thus highly desirable yet remains formidably challenging. Herein, we synthesized two novel N‐heteroacenes‐based NIR‐II luminogens, namely 2TT‐PPT and 4TT‐PBPT, by respectively employing pyrene‐fused phenaziothiadiazoles and pyrene‐fused bisphenaziothiadiazoles as acceptor skeletons. There is strength in numbers by increasing the fusing rings in N‐heteroacenes moieties and numbers of appended donors. Compared to less ring‐fused 2TT‐PPT, the giant molecule 4TT‐PBPT shows improved photophysical characteristics, such as enhanced light absorbance, red‐shifted wavelengths, higher brightness, favorable reactive oxygen species (ROS) generation, and elevated photothermal conversion efficiency, which render 4TT‐PBPT nanoparticles excellent fluorescence‐photoacoustic‐photothermal trimodal imaging guided photodynamic‐photothermal synergistic therapy for orthotopic bladder cancer.

Strength in Numbers: A Giant NIR-II AIEgen with One-for-All Phototheranostic Features for Exceptional Orthotopic Bladder Cancer Treatment.

One-for-all phototheranostics that allows the simultaneous implementations of multiple optical imaging and therapeutic modalities by utilizing a single component, is growing into a sparkling frontier in cancer treatment. Of particular interest is phototheranostic agent with emission in the second near-infrared (NIR-II) window. Nevertheless, the practical uses of those conventional NIR-II agents are severely impeded by their unsatisfactory features including insufficient stability, low synthetic yield, to be extended absorption/ emission wavelengths, and inefficient phototheranostic outcomes. Developing exceptional phototheranostic agents is thus highly desirable yet remains formidably challenging. Herein, we synthesized two novel N-heteroacenes-based NIR-II luminogens, namely 2TT-PPT and 4TT-PBPT, by respectively employing pyrene-fused phenaziothiadiazoles and pyrene-fused bisphenaziothiadiazoles as acceptor skeletons. There is strength in numbers by increasing the fusing rings in N-heteroacenes moieties and numbers of appended donors. Compared to less ring-fused 2TT-PPT, the giant molecule 4TT-PBPT shows improved photophysical characteristics, such as enhanced light absorbance, red-shifted wavelengths, higher brightness, favorable reactive oxygen species (ROS) generation, and elevated photothermal conversion efficiency, which render 4TT-PBPT nanoparticles excellent fluorescence-photoacoustic-photothermal trimodal imaging guided photodynamic-photothermal synergistic therapy for orthotopic bladder cancer.

Remote Sensing Identification and Information Extraction Method of Glacial Debris Flow Based on Texture Variation Characteristics

The formation mechanism of glacial debris flows in alpine gorge mountain areas is complex, with varying characteristics across different regions. Due to the influence of mountain shadows and the accumulation and ablation of ice and snow, accurately identifying and rapidly extracting glacial debris flows using optical images remains challenging. This study utilizes the Random Forest method to develop a multi-feature spatiotemporal information extraction model based on Landsat-8 images and a glacial debris flow gully identification model. These models were applied to the Songzong–Tongmai section of the Sichuan–Tibet Highway to identify glacial debris flows. The results showed that (1) the multi-feature spatiotemporal extraction model effectively eliminated the interference of mountain shadows and ice–snow phase changes, resulting in a higher accuracy for identifying and extracting glacial debris flows in areas with significant information loss due to deep shadows. The total accuracy was 93.6%, which was 8.9% and 4.2% higher than that of the Neural Network and Support Vector Machine methods, respectively. (2) The accuracy of the glacial debris flow gully identification model achieved 92.6%. The proposed method can accurately and rapidly identify glacial debris flows in alpine gorge mountain areas, facilitating remote sensing dynamic monitoring. This approach reduces the damage caused by debris flows to both transportation and the environment, ensuring the safe passage of highways and promoting the sustainable development of the region.

Activation of Ms 6.9 Milin Earthquake on Sedongpu Disaster Chain, China with Multi-Temporal Optical Images

In recent years, disaster chains caused by glacier movements have occurred frequently in the lower Yarlung Tsangpo River in southwest China. However, it is still unclear whether earthquakes significantly contribute to glacier movements and disaster chains. In addition, it is difficult to measure the high-frequency and large gradient displacement time series with optical remote sensing images due to cloud coverage. To this end, we take the Sedongpu disaster chain as an example, where the Milin earthquake, with an epicenter 11 km away, occurred on 18 November 2017. Firstly, to deal with the cloud coverage problem for single optical remote sensing analysis, we employed multiple platform optical images and conducted a cross-platform correlation technique to invert the two-dimensional displacement rate and the cumulative displacement time series of the Sedongpu glacier. To reveal the correlation between earthquakes and disaster chains, we divided the optical images into three classes according to the Milin earthquake event. Lastly, to increase the accuracy and reliability, we propose two strategies for displacement monitoring, that is, a four-quadrant block registration strategy and a multi-window fusion strategy. Results show that the RMSE reduction percentage of the proposed registration method reaches 80%, and the fusion method can retrieve the large magnitude displacements and complete displacement field. Secondly, the Milin earthquake accelerated the Sedongpu glacier movement, where the pre-seismic velocities were less than 0.5 m/day, the co-seismic velocities increased to 1 to 6 m/day, and the post-seismic velocities decreased to 0.5 to 3 m/day. Lastly, the earthquake had a triggering effect around 33 days on the Sedongpu disaster chain event on 21 December 2017. The failure pattern can be summarized as ice and rock collapse in the source area, large magnitude glacier displacement in the moraine area, and a large volume of sediment in the deposition area, causing a river blockage.

Hybrid Space Calibrated 3D Network of Diffractive Hyperspectral Optical Imaging Sensor.

Diffractive multispectral optical imaging plays an essential role in optical sensing, which typically suffers from the image blurring problem caused by the spatially variant point spread function. Here, we propose a novel high-quality and efficient hybrid space calibrated 3D network "HSC3D" for spatially variant diffractive multispectral imaging that utilizes the 3D U-Net structure combined with space calibration modules of magnification and rotation effects to achieve high-accuracy eight-channel multispectral restoration. The algorithm combines the advantages of the space calibrated module and U-Net architecture with 3D convolutional layers to improve the image quality of diffractive multispectral imaging without the requirements of complex equipment modifications and large amounts of data. A diffractive multispectral imaging system is established by designing and manufacturing one diffractive lens and four refractive lenses, whose monochromatic aberration is carefully corrected to improve imaging quality. The mean peak signal-to-noise ratio and mean structural similarity index of the reconstructed multispectral images are improved by 3.33 dB and 0.08, respectively, presenting obviously improved image quality compared with a typical Unrolled Network algorithm. The new algorithm with high space calibrated ability and imaging quality has great application potential in diffraction lens spectroscopy and paves a new method for complex practical diffractive multispectral image sensing.

Cylindrical Vector Beams with an MOPA Amplifier Based on Nonlinear Polarization Rotation Mode-Locking

CVBs (cylindrical vector beams) are widely used in optical imaging, optical trapping, material processing, etc. In this study, based on mode-selective couplers and passive mode-locking fiber technology, a cylindrical vector fiber amplifier with an MOPA (master oscillator power amplifier) structure was developed. In the experiment, the pre-amp stage reached 19.87 mW output power and a CVB output with a mode purity greater than 97%. The measured beam quality factor was M2 = 2.1. The CVB output power obtained by the first-amp stage was 152.4 mW, and the mode purity was greater than 92%. The measured beam quality factor was M2 = 1.99. The internal inhomogeneity and external effects of the isotropic LMA (large-mode-area) fiber led to a decrease in beam quality and mode purity. After amplification, the gain of the fundamental mode was higher and the power was greater, resulting in a greatly reduced mode purity. This CVB fiber amplifier yielded important research value in expanding the applications of high-power fiber lasers.

A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE)

We examine the prevalence of truncated star-forming discs in the Virgo cluster down to M* ≃ 107 M⊙. This work makes use of deep, high-resolution imaging in the Hα+[N II] narrow-band from the Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE) and optical imaging from the Next Generation Virgo Survey (NGVS). To aid in the understanding of the effects of the cluster environment on star formation in Virgo galaxies, we take a physically motivated approach to define the edge of the star-forming disc via a drop-off in the radial specific star formation rate profile. A comparison with the expected sizes of normal galactic discs provides a measure of how truncated star-forming discs are in the cluster. We find that truncated star-forming discs are nearly ubiquitous across all regions of the Virgo cluster, including beyond the virial radius (0.974 Mpc). The majority of truncated discs at large cluster-centric radii are of galaxies likely on their first infall. As the intra-cluster medium density is low in this region, it is difficult to explain this population with solely ram-pressure stripping. A plausible explanation is that these galaxies are undergoing starvation of their gas supply before ram-pressure stripping becomes the dominant quenching mechanism. A simple model of starvation shows that this mechanism can produce moderate disc truncations within 1−2 Gyr. This model is consistent with ‘slow-then-rapid’ or ‘delayed-then-rapid’ quenching, whereby the early starvation mode drives disc truncations without significant change to the integrated star formation rate, and the later ram-pressure stripping mode rapidly quenches the galaxy. The origin of starvation may be in the group structures that exist around the main Virgo cluster, which indicates the importance of understanding pre-processing of galaxies beyond the cluster virial radius.

Self-supervised based clustering for retinal optical coherence tomography images.

In response to the inadequacy of manual analysis in meeting the rising demand for retinal optical coherence tomography (OCT) images, a self-supervised learning-based clustering model was implemented. A public dataset was utilized, with 83,484 OCT images with categories of choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal fundus. This study employed the Semantic Pseudo Labeling for Image Clustering (SPICE) framework, a self-supervised learning-based method, to cluster unlabeled OCT images into binary and four categories, and the performances were compared with baseline models. We also analysed feature distribution using t-SNE, and explored the cluster centers, attention maps, and misclassified images. In addition, DME and CNV subsets were clustered binarily, and the results were interpreted by two retinal specialists. SPICE demonstrated superior performance in binary and four categories classification tasks, achieving the accuracy of 0.886 and 0.846, respectively. In t-SNE analysis, the four types exhibited significant clustering into distinct groups. The cluster centers corresponded to the human labels, and the heat map revealed that the model focused on important biomarkers. The misclassified images exposed similar features to the inaccurate classes. The model also grouped DME and CNV into two distinct categories respectively. Self-supervised clustering effectively distinguished disease variances and revealed common features, with a notable capability to detect disease heterogeneity through biomarkers.

Adaptive-modulated fast fluctuation super-resolution microscopy

Fluorescence microscopy has significantly advanced biological imaging at the nanoscale, particularly with the advent of super-resolution microscopy (SRM), which transcends the Abbe diffraction limit. Most cutting-edge SR methods require high-precision optical setups, which constrain the widespread adoption of SRM. Fluorescence fluctuation-based SRM (FF-SRM) can break the diffraction limit without complex optical components, making it particularly well-suited for biological imaging. However, conventional FF-SRM methods, such as super-resolution optical fluctuation imaging (SOFI), still require specific fluorescent molecular blinking properties. Instead of enhancing the intrinsic blinking characteristics by finding specific fluorescent markers, employing optical methods such as spatial light modulation to adjust the excitation light field allows for easier and more flexible matching of the on-time ratio with the analysis of temporal stochastic intensity fluctuations. Nevertheless, the specific parameters of the modulation patterns have not been thoroughly explored, despite their crucial influence on the reconstruction quality. Herein, we propose adaptive-modulated fast fluctuation super-resolution microscopy. Our method demonstrates theoretically and experimentally that restricting the size of modulation units in a certain range ensures better image quality with fewer artifacts and signal losses. We find it still significantly effective when applied to other FF-SRM. Overall, the further development of the adaptive modulation technique has made it more stable in behavior and maintained high-quality imaging, presenting broader prospects for super resolution imaging based on statistical analysis.

Characterization of plaque hemorrhage with optical coherence tomography imaging in statin-treated patients with coronary artery disease: comparison with noncontrast T1-weighted MRI

Abstract Background Plaque hemorrhage is a phenotypic feature of coronary lesions associated with future coronary events. Noncontrast T1-weighted MRI has a potential to non-invasively identify plaque hemorrhage. Plaque to myocardium ratio (PMR) evaluated by noncontrast T1-weighted MRI has been shown to pathohistologically correspond to plaque hemorrhage. In several case reports comparing OCT images with coronary specimens, low intensity area without its attenuation was observed at lesions presenting plaque hemorrhage. However, whether the association of this OCT-derived feature with PMR remains to be elucidated yet. Purpose To characterize plaque hemorrhage in vivo by comparing with OCT and noncontrast T1-weighted MRI. Methods The current study prospectively enrolled 80 statin-treated patients with coronary artery disease requiring elective PCI. A total of 103 target lesions were imaged by both noncontrast T1-weighted MRI and OCT prior to PCI. Plaque hemorrhage on noncontrast T1-weighted MRI was defined as PMR>1.0. On OCT imaging analysis, low intensity area without attenuation (LIA) was defined according to the following criteria; (1) area presenting homogeneous low intensity signals, (2) clear border and (3) no signal attenuation behind its area. The maximum arc of LIA was manually measured. OCT-derived plaque features were compared in lesions with and without PMR>1.0. Results PMR>1.0 was observed at 49.5% of analyzed lesions. Patients with PMR>1.0 were more likely older (72 vs. 66 years, p=0.02), whereas there was no significant difference in on-treatment LDL-C levels (median: 80 vs. 77 mg/dL, p=0.62) (Table). On lesion-based analysis, while fibrous cap thickness was similar between the two groups (80 vs. 100um, p=0.10), a larger lipid arc (270 vs. 230º, p=0.02) and a greater frequency of macrophage (90.2 vs. 53.9%, p<0.01), layered plaque (62.8 vs. 32.7%, p=0.003) and cholesterol crystal (70.6 vs. 30.8%, p<0.001) were observed at lesions with PMR>1.0. Furthermore, lesions with PMR>1.0 more frequently exhibited LIA (66.7 vs. 19.2%, p<0.001) and its greater maximum arc (33 vs. 0º, p<0.001) (Figure). Of note, PMR was significantly correlated to the maximum arc of LIA (r=0.51, p<0.001) (Figure). Multivariate analysis demonstrated LIA and cholesterol crystal as independent OCT-derived features associated with PMR>1.0 (Table). Conclusion In statin-treated patients, 49.5% of analyzed lesions presented PMR>1.0. Coronary lesions with PMR>1.0 more frequently harbored LIA and cholesterol crystals, in addition to other vulnerable OCT features. Our findings suggest OCT-derived LIA as a potential signature of plaque hemorrhage. Given that cholesterol crystal was another feature of PMR>1.0, plaque inflammation driven by cholesterol crystallization may exist at plaque hemorrhage despite statin use. Whether anti-inflammation agent could modulate plaque hemorrhage requires future investigation.

Plaque phenotyping using deep learning in optical coherence tomography imaging

Abstract Background Artificial intelligence (AI) techniques have emerged as powerful tools in various computer vision tasks, particularly in classification, detection, and segmentation. Purpose This study aimed to develop a deep learning model for categorizing optical coherence tomography (OCT) frames into different plaque phenotypes, including thin-cap fibroatheroma (TCFA) and fibroatheroma. Methods A prospective multicenter observational study enrolled 168 patients who underwent percutaneous coronary intervention (PCI) and received OCT imaging for non-target lesions with angiographically estimated diameter stenosis greater than 30% (177 vessels). Among them, 129 patients (135 vessels) underwent follow-up OCT imaging at 1 year. A total of 45,072 OCT frames were randomly divided into training and validation sets in a 4:1 ratio. An EfficientNet-B3 convolutional neural network model was developed to classify OCT frames into TCFA, thick-cap fibroatheroma, non-fibroatheroma, adaptive intimal thickening, and normal vessel categories. Results A confusion matrix comparing manually assessed plaque phenotypes with AI-predicted phenotypes was presented in Table. In the validation datasets (9,015 frames), the overall accuracy of the model was 85.1%. For predicting the presence of TCFA, sensitivity was 81.1%, specificity was 98.8%, positive predictive value was 63.9%, and negative predictive value was 99.5%. Regarding the presence of fibroatheroma, sensitivity was 90.3%, specificity was 89.4%, positive predictive value was 93.1%, and negative predictive value was 85.4%. Conclusions The deep learning algorithm demonstrated high accuracy in detecting TCFA and fibroatheroma with excellent reproducibility. This automated process enables operators to promptly identify high-risk lesions during PCI, potentially leading to improved clinical outcomes.

Polarization remote sensing of ships in sea haze

ABSTRACT Sea haze reduces the visibility of ships and makes the optical imaging system blind, which increases the difficulty of ship remote sensing. In this paper, we propose a new method for polarization remote sensing of ships in sea haze. The method takes advantage of image haze removal and exposure correction to improve the visibility of ships in sea haze. Different from the existing polarization-based image haze removal methods, the proposed method does not depend on any assumptions about the polarization properties of incident light. The exposure correction method is proposed based on the Retinex model and Bayes theorem, which can improve the visibility of ships in haze removal images. To evaluate the effectiveness of the proposed method, we implemented an outdoor experiment on polarization remote sensing of ships in sea haze weather. The experimental results indicate that the method can improve the visibility of ships significantly. In addition, the method outperforms the Stokes parameter-based ship remote sensing method in terms of the image quality metric fog aware density evaluator and can be easily generalized to RGB image haze removal.

Label-Free Optical Transmission Tomography for Direct Mycological Examination and Monitoring of Intracellular Dynamics

Live-cell imaging generally requires pretreatment with fluorophores to either monitor cellular functions or the dynamics of intracellular processes and structures. We have recently introduced full-field optical coherence tomography for the label-free live-cell imaging of fungi with potential clinical applications for the diagnosis of invasive fungal mold infections. While both the spatial resolution and technical set up of this technology are more likely designed for the histopathological analysis of tissue biopsies, there is to our knowledge no previous work reporting the use of a light interference-based optical technique for direct mycological examination and monitoring of intracellular processes. We describe the first application of dynamic full-field optical transmission tomography (D-FF-OTT) to achieve both high-resolution and live-cell imaging of fungi. First, D-FF-OTT allowed for the precise examination and identification of several elementary structures within a selection of fungal species commonly known to be responsible for invasive fungal infections such as Candida albicans, Aspergillus fumigatus, or Rhizopus arrhizus. Furthermore, D-FF-OTT revealed the intracellular trafficking of organelles and vesicles related to metabolic processes of living fungi, thus opening new perspectives in fast fungal infection diagnostics.

Multimodal Optical Imaging of Ex Vivo Fallopian Tubes to Distinguish Early and Occult Tubo-Ovarian Cancers.

Background: There are currently no effective screening measures to detect early or occult tubo-ovarian cancers, resulting in late-stage detection and high mortality. This work explores whether an optical imaging catheter can detect early-stage tubo-ovarian cancers or precursor lesions where they originate in the fallopian tubes. Methods: This device collects co-registered optical coherence tomography (OCT) and autofluorescence imaging (AFI). OCT provides three-dimensional assessment of underlying tissue structures; autofluorescence imaging provides functional contrast of endogenous fluorophores. Ex vivo fallopian tubes (n = 28; n = 7 cancer patients) are imaged; we present methods for the calculation of and analyze eleven imaging biomarkers related to fluorescence, optical attenuation, and OCT texture for their potential to detect tubo-ovarian cancers and other lesions of interest. Results: We visualize folded plicae, vessel-like structures, tissue layering, hemosiderin deposits, and regions of fibrotic change. High-grade serous ovarian carcinoma appears as reduced autofluorescence paired with homogenous OCT and reduced mean optical attenuation. Specimens containing cancerous lesions demonstrate a significant increase in median autofluorescence intensity and decrease in Shannon entropy compared to specimens with no lesion. Non-cancerous specimens demonstrate an increase in optical attenuation in the fimbriae when compared to the isthmus or the ampulla. Conclusions: We conclude that this approach shows promise and merits further investigation of its diagnostic potential.