Imaging Methods Research Articles

Field test of internal visualization of deep-water pile foundation using ultrasonic waves

Early-age internal visualization is critical for timely assessing the quality of pile foundations, especially as super cross-sea bridges increase the diameter and length of pile foundations beyond the capabilities of traditional cross-hole sonic logging (CSL). This article introduces two innovations. First, ultrasonic instruments are specially developed to transmit waves through a concrete pile with a diameter of 4 m, a length of 92 m, and a water depth of 63 m, before the full hardening of concrete. Second, an imaging method is proposed to upgrade traditional CSL from 1D detection to 3D visualization. Both large-scale experiments and field tests are tested in the first 7 days after the concrete casting. The experiments successfully visualize typical construction defects, achieving a detection resolution of 18 cm for layered defects and 15 cm for void defects. During the field test, a low-pixel region is observed at the pile head, extending approximately 2.1 m deep, attributed to mud floating up during concrete casting into the steel casing. These developments in instrument technology and visualization methods provide essential tools for the quality evaluation of super pile foundations. Furthermore, the visualization data can support digital twin modeling in subsequent stages of construction and maintenance planning.

A method for extracting Martian terrain features based on unsupervised contrastive learning

Intelligent recognition of Martian surface terrain is of great significance to autonomous exploration of Mars rovers. Feature extraction methods for Martian terrain images are currently mainly divided into two categories: traditional shallow visual feature extraction and deep feature extraction based on supervised learning. Loss of image information and the need for a large amount of labeled data are key issues to be addressed. A method for Martian terrain feature recognition based on unsupervised contrastive learning is proposed. By establishing an image dictionary dataset, two sets of neural networks, "query" and "encoding", are used to compare a single image with other images in the "dictionary" dataset, and the network is trained using the similarity functional as the loss function, thereby realizing feature recognition of Martian terrain images. The proposed method also has the ability to recognize new types of terrain images outside the training dataset, and has outstanding superiority in subsequent recognition and classification. Computer simulation verification shows that the recognition accuracy of the proposed method is and the 85.4%recognition accuracy of new types of terrain images is 84.5%.

Contraband classification method for X-ray security images considering sample imbalance

X-ray security image contraband classification is widely used to assist in maintaining aviation and transportation security. This paper suggests an end-to-end X-ray security inspection image classification method that takes sample imbalance into account in order to address the issues of different scales of contraband in X-ray images, challenging samples, and unbalanced positive and negative samples inherent in passenger baggage security inspection. The feature fusion module is used to enhance the model's ability to express picture edge and texture features while the multi-scale feature extraction network is used to capture the features of numerous sorts of illegal goods with various scales. Based on the cost-sensitive idea, the loss function is designed to solve the problem of dataset imbalance, and improve the classification accuracy of difficult samples. experimental results of the subset constructed on the public dataset SIXray show that the proposed method improves the mean AP index by compared with the current optimal end-to-end classification model, especially for hard-to-classify samples such as scissors, the AP index has a significant improvement effect.

Protecting Drawings at Production and Regime Facilities Using Neural Network Technology and DLP Systems

The existing methods of object image processing are analyzed. Problems of using the considered methods within the framework of DLP systems are considered. A new object image processing method was presented that allows processing of complex images. A metric of accuracy and completeness of plagiarism image detection was used to evaluate the quality of the developed method. Testing was carried out using image ranking to analyze the ability of the model to search for semantics. Comparative testing was carried out with the method based on raster neural models. The advantages and disadvantages of the developed method were highlighted, as well as options for further developmentAs a result of this work, a method for processing object images in native format has been developed. The main advantage of the developed method is the high rate of accuracy (87 %) and completeness (100 %). This study can be useful for further research in the field of vector image analysis. Also, the developed method can be applied as a tool similarly to raster methods of image processing (image search, classification, search for objects in the image).

Real-time ultrasound AR 3D visualization toward better topological structure perception for hepatobiliary surgery.

Ultrasound serves as a crucial intraoperative imaging tool for hepatobiliary surgeons, enabling the identification of complex anatomical structures like blood vessels, bile ducts, and lesions. However, the reliance on manual mental reconstruction of 3D topologies from 2D ultrasound images presents significant challenges, leading to a pressing need for tools to assist surgeons with real-time identification of 3D topological anatomy. We propose a real-time ultrasound AR 3D visualization method for intraoperative 2D ultrasound imaging. Our system leverages backward alpha blending to integrate multi-planar ultrasound data effectively. To ensure continuity between 2D ultrasound planes, we employ spatial smoothing techniques to interpolate the widely spaced ultrasound planes. A dynamic 3D transfer function is also developed to enhance spatial representation through color differentiation. Comparative experiments involving our AR visualization of 3D ultrasound, alongside AR visualization of 2D ultrasound and 2D visualization of 3D ultrasound, demonstrated that the proposed method significantly reduced operational time(110.25 ± 27.83s compared to 292 ± 146.63s and 365.25 ± 131.62s), improved depth perception and comprehension of complex topologies, contributing to reduced pressure and increased personal satisfaction among users. Quantitative experimental results and feedback from both novice and experienced physicians highlight our system's exceptional ability to enhance the understanding of complex topological anatomy. This improvement is crucial for accurate ultrasound diagnosis and informed surgical decision-making, underscoring the system's clinical applicability.

Alpine SOC and Microbial Community Assembly Were Buffered Through Soil Pore Structure Along an Altitudinal Gradient

ABSTRACTElevation changes influence various environmental factors including cloudiness, atmospheric density, and temperature. Previous studies on the effects of elevation on microbial communities and soil organic carbon (SOC) yielded inconsistent results. This study tried to reveal the distribution patterns of microbial communities and SOC concentrations, as well as their interactions with soil structure along an elevational gradient in the alpine region. We investigated six typical ecosystems along an elevational gradient on the north‐eastern Qinghai‐Tibet Plateau. Phospholipid fatty acid (PLFA) analysis and X‐ray computed tomography (CT) methods were used to quantify microbial abundance and pore structure of soils, respectively. The results demonstrated that SOC content and total PLFAs peaked in the meadow ecosystem. In the subsoil, total PLFAs, fungal, and bacterial PLFAs followed the U‐shape pattern with increasing elevation. In both topsoils and subsoils, the surface area density of pores increased with elevation, and it was found to be positively correlated with SOC and microbial abundance. Soil structure mainly affects the input and adsorption of root nutrients by altering the pore surface area, thereby regulating the enrichment of microorganisms. The impact of pore structure on microbes were more obvious in the topsoil than in the subsoil. Interactions among pore structure, soil properties, and environmental factors jointly affects the microbial communities, demonstrating that elevation indirectly affects microbial communities through soil resource regulation.

Kilohertz volumetric imaging of in-vivo dynamics using squeezed light field microscopy.

Volumetric functional imaging of transient cellular signaling and motion dynamics poses a significant challenge to current microscopy techniques, primarily due to limitations in hardware bandwidth and the restricted photon budget within short exposure times. In response to this challenge, we present squeezed light field microscopy (SLIM), a computational imaging method that enables rapid detection of high-resolution three-dimensional (3D) light signals using only a single, low-format camera sensor area. SLIM pushes the boundaries of 3D optical microscopy, achieving over one thousand volumes per second across a large field of view of 550 μm in diameter and 300 μm in depth with a spatial resolution of 3.6 μm laterally and 6 μm axially. Using SLIM, we demonstrated blood cell velocimetry across the embryonic zebrafish brain and in a free-moving tail exhibiting high-frequency swinging motion. The millisecond temporal resolution also enables accurate voltage imaging of neural membrane potentials in the leech ganglion, and in the hippocampus of behaving mice. These results collectively establish SLIM as a versatile and robust imaging tool for high-speed microscopy applications.

Exploratory study on a novel minimally invasive tunnel like flap approach for orthodontic movement of dental implants.

To propose a new flap design that utilize a minimally invasive approach to move implants. We also propose a 3D method to track changes in implant position without the need for CBCT. Implants were inserted in two mongrel dogs then a minimally invasive tunnel like flap was made. Bone cutting around implants was done using piezotome device and orthodontic force was applied on the implants. For tracking positional changes of implants, an impression was taken then scanned with an identical implant copy inside. The pre- and post-treatment 3D images were then superimposed over each other to detect changes in implant position. Healing of the flap was rapid with minimal swelling and edema. The position of the interdental papilla was preserved maintaining optimal esthetic outcome. Implant pocket depth was maintained. Our 3D imaging method was able to accurately detect the movement that occurred in the dental implants after their movement with significantly higher resolution than CBCT (6μm for this method versus 76-300μm for the CBCT). This flap can preserve the esthetic soft tissue contour of implants during orthodontic movement allowing rapid healing with minimum swelling and edema. Moreover, our proposed 3D method has many advantages over CBCT in tracking the movement of implants by being non harmful and cheap thus allowing several follow up points. Orthodontic movement of implants can be required in cases of improperly placed implants or to intentionally place the implant in a certain position then altering it to avoid the need for bone grafting.

Advancing Graphene Imaging for Clear Identification of Lattice Defects: The Application of Revolve Sphere Levelling to Scanning Tunnelling Microscopy Images.

Application of revolve sphere levelling (RSL) as a practical and effective image processing tool for enhancing scanning tunnelling microscopy (STM) images of graphene atomic lattices is presented. Low-cost, ambient, and non-invasive STM methods overcome limitations of traditional imaging methods like scanning transmission electron microscopy (STEM) and transmission electron microscopy (TEM) that can introduce or alter defects in graphene. Utilizing high-quality graphene synthesized via Paragraf's patented Metal-Organic Chemical Vapor Deposition (MOCVD) method, RSL, which is easily implemented via the Gwyddion software package, effectively highlights the hexagonal lattice structure and specific defect structures. This provides clarity of the atomic structure that traditional methods struggle to achieve. This research emphasizes the utility of RSL in materials science for defect identification in graphene, and points to future research in optimizing RSL for a broader range of defects and applications in other 2D materials.

Real-time high-resolution microscopy reveals how single-cell lysis shapes biofilm matrix morphogenesis.

During development, multiscale patterning requires that cells organize their behavior in space and time. Bacteria in biofilms must similarly dynamically pattern their behavior with a simpler toolkit. Like in eukaryotes, morphogenesis of the extracellular matrix is essential for biofilm development, but how it is patterned has remained unclear. Here, we explain how the architecture of eDNA, a key matrix component, is controlled by single cell lysis events during Pseudomonas aeruginosa biofilm development. We extend single-cell imaging methods to capture complete biofilm development, characterizing the stages of biofilm development and visualizing eDNA matrix morphogenesis. Mapping the spatiotemporal distribution of single cell lysis events reveals that cell lysis is restricted to a specific biofilm zone. Simulations indicate that this patterning couples cell lysis to growth, more uniformly distributing eDNA throughout the biofilm. Finally, we find that patterning of cell lysis is organized by nutrient gradients that act as positioning cues.

Real-Time Imaging of Calcium Dynamics in Human Sperm After Precise Single-Cell Stimulation.

Calcium signaling is a critical regulator of sperm activation and function during the processes of capacitation and fertilization. Here, we describe a combined method for calcium imaging of single, live human sperm in response to stimuli administered with a precisely targeted delivery technique. This protocol is an adaptation of techniques developed for studies of murine sperm [1, 2], and enables real-time monitoring of human sperm calcium dynamics with high spatiotemporal resolution and concurrent detection of acrosome exocytosis (AE), a functional endpoint of sperm capacitation and requirement for physiological fertilization.The described imaging technique provides a valuable tool for exploration of calcium regulation in human sperm, which is essential to answer important questions and knowledge gaps regarding the link between calcium dynamics, AE, and fertilization. The versatility of this technique can be amplified through use of various indicator dyes or integration with pharmacological strategies such as pre-treating sperm with inhibitors or activators targeting specific receptors, channels, or intracellular signaling pathways of interest. Beyond fundamental inquiries into sperm physiology, this method can also be applied to assess the impact of potential contraceptive compounds on calcium signaling, AE, and membrane integrity.

Multi-frequency instantaneous wavenumber damage imaging method based on ultrasonic guided waves induced by laser point-by-point excitation

ABSTRACT A multi-frequency instantaneous wavenumber damage imaging method based on ultrasonic guided waves induced by laser point-by-point excitation is proposed in this paper. The experiment involved using the Nd:YAG pulsed laser is used as the excitation source to excite ultrasonic guided waves point-by-point in the detection area, and the piezoelectric ceramic transducer is used to receive ultrasonic guided wavefield at a fixed position. The wavefield information at different frequencies is extracted by a three-dimensional Fourier transform. The spatial phase distribution of the extracted guided wavefield with different frequencies is obtained by the Hilbert transform and phase unwrapping method. Then, the wavenumber of each detection point and the dispersion relations of the test piece with different thicknesses are calculated. Based on the calculated dispersion relation, the mapping relationship between the wavenumber and the thickness of the test piece is obtained. Furthermore, based on the obtained wavenumber information and wavenumber thickness mapping relationship, the thickness reduction depth of the defect can be calculated. Finally, the quantitative detection of defects is achieved through data fusion. The proposed method is verified through quantitative detection of the rectangular groove defects and the flat bottom defects with variable thickness and complex contour in the aluminium plate. At the same time, the detection results of the proposed method are compared with those of the single-frequency instantaneous wavenumber imaging method, single-frequency local wavenumber imaging method, and multi-frequency local wavenumber imaging method. In the detection results, the method proposed in this paper presents a better defect detection effect.

Research on the fusion imaging method of sign coherence and time reversal for Lamb wave sparse array

Time-reversal imaging struggles to detect plate-like structures due to interference from Lamb wave mode conversion and the processing demands, leading to less effective outcomes. This paper proposes a sign coherence factor and time reversal fusion (SCF-TR) imaging method based on amplitude and phase estimation. This method removes the coherence of array signals during signal reversal and refocusing. It reintroduces the sign coherence component to reduce interference from non-target scattered waves and partially overcome the constraints imposed by the Rayleigh criterion. The method allows imaging at a resolution smaller than the wavelength of Lamb and enhances the quality of the resulting images. In addition, a sparse array design utilizing the White Shark Optimisation Algorithm (WSO) is proposed to streamline the SCF-TR calculation process. This design utilizes sparse full matrix data to improve imaging efficiency. The experimental results show that for single blind hole defects, the SCF-TR method improves the array performance metrics and signal-to-noise ratio by 22.46% and 42.50%, respectively, compared to the TR method. For multiple asymmetric blind hole defects, when the defect size exceeds the resolution threshold, SCF-TR accurately reflects the position and morphology of defects smaller than the wavelength. When the defect size is below the resolution threshold, SCF-TR achieves super-resolution imaging. The sparse array designed using the White Shark Optimization algorithm demonstrates good sidelobe characteristics, effectively reducing sidelobe noise without reducing the array aperture. Moreover, the SCF-TR imaging time is reduced by approximately half while maintaining imaging accuracy.

Underwater Optical Imaging: Methods, Applications and Perspectives

Underwater optical imaging is essential for exploring the underwater environment to provide information for planning and regulating underwater activities in various underwater applications, such as aquaculture farm observation, underwater topographical survey, and underwater infrastructure monitoring. Thus, there is a need to investigate the underwater imaging process and propose clear and long-range underwater optical imaging methods to fulfill the demands of academia and industry. In this manuscript, we classify the eighteen most commonly used underwater optical imaging methods into two groups regarding the imaging principle, (1) hardware and (2) software-based methods, each with an explanation of the theory, features, and applications. Furthermore, we also discuss the current challenges and future directions for improving the performance of current methods, such as improving the accuracy of underwater image formation model estimation, enlarging the underwater image dataset, proposing comprehensive underwater imaging evaluation metrics, estimating underwater depth and integrating different methods (e.g., hardware- and software-based methods for computational imaging) to promote the imaging performance not only in the laboratory but also in practical underwater scenarios.

Palette‐based colour normalization for histopathology images

AbstractWith the emergence of computer‐aided diagnostic (CAD) systems, the speed of histopathological image analysis and the accuracy of cancer detection have considerably improved. However, the appearance of histopathological slides can vary depending on the consistency of tissue thickness, stain concentrations, and equipment, thus affecting the judgment of CAD systems. This study proposes a palette‐based colour normalization method for histopathology images is proposed to solve the problem of colour differences between histopathology images. The method builds a graphical user interface based on an improved palette generation algorithm and colour transfer definitions, through which the user can complete the corresponding colour modification to achieve colour normalization. The evaluation of the metrics on histopathological images shows that our method is able to achieve higher image quality and better preservation of structural information of the source image compared with four other colour normalization algorithms. The peak signal‐to‐noise ratio values obtained by the proposed method on two publicly available datasets were 24.1914 and 21.3666, and the structural similarity index matrix values were 0.9871 and 0.9760. The proposed method provides new ideas for the development and design of CAD systems.

Prediction of stroke severity: systematic evaluation of lesion representations.

To systematically evaluate which lesion-based imaging features and methods allow for the best statistical prediction of poststroke deficits across independent datasets. We utilized imaging and clinical data from three independent datasets of patients experiencing acute stroke (N1 = 109, N2 = 638, N3 = 794) to statistically predict acute stroke severity (NIHSS) based on lesion volume, lesion location, and structural and functional disconnection with the lesion location using normative connectomes. We found that prediction models trained on small single-center datasets could perform well using within-dataset cross-validation, but results did not generalize to independent datasets (median R2 N1 = 0.2%). Performance across independent datasets improved using large single-center training data (R2 N2 = 15.8%) and improved further using multicenter training data (R2 N3 = 24.4%). These results were consistent across lesion attributes and prediction models. Including either structural or functional disconnection in the models outperformed prediction based on volume or location alone (P < 0.001, FDR-corrected). We conclude that (1) prediction performance in independent datasets of patients with acute stroke cannot be inferred from cross-validated results within a dataset, as performance results obtained via these two methods differed consistently, (2) prediction performance can be improved by training on large and, importantly, multicenter datasets, and (3) structural and functional disconnection allow for improved prediction of acute stroke severity.

Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer.

Background/Objectives: Head and neck cancer (HNC), predominantly squamous cell carcinoma (SCC), presents a significant global health burden. Conventional diagnostic approaches often face challenges in terms of achieving early detection and accurate diagnosis. This review examines recent advancements in hyperspectral imaging (HSI), integrated with computer-aided diagnostic (CAD) techniques, to enhance HNC detection and diagnosis. Methods: A systematic review of seven rigorously selected studies was performed. We focused on CAD algorithms, such as convolutional neural networks (CNNs), support vector machines (SVMs), and linear discriminant analysis (LDA). These are applicable to the hyperspectral imaging of HNC tissues. Results: The meta-analysis findings indicate that LDA surpasses other algorithms, achieving an accuracy of 92%, sensitivity of 91%, and specificity of 93%. CNNs exhibit moderate performance, with an accuracy of 82%, sensitivity of 77%, and specificity of 86%. SVMs demonstrate the lowest performance, with an accuracy of 76% and sensitivity of 48%, but maintain a high specificity level at 89%. Additionally, in vivo studies demonstrate superior performance when compared to ex vivo studies, reporting higher accuracy (81%), sensitivity (83%), and specificity (79%). Conclusion: Despite these promising findings, challenges persist, such as HSI's sensitivity to external conditions, the need for high-resolution and high-speed imaging, and the lack of comprehensive spectral databases. Future research should emphasize dimensionality reduction techniques, the integration of multiple machine learning models, and the development of extensive spectral libraries to enhance HSI's clinical utility in HNC diagnostics. This review underscores the transformative potential of HSI and CAD techniques in revolutionizing HNC diagnostics, facilitating more accurate and earlier detection, and improving patient outcomes.

Pricing of timer volatility-barrier options under Heston’s stochastic volatility model

Timer options are financial instruments that enable investors to exercise their rights on a random maturity date the realized variance reaches the level of variance budget. These options provide a stable investment opportunity for investors under the unpredictable and complex financial markets, such as global financial crisis or COVID-19 pandemic, which can induce the drastic changes of the volatility for the underlying asset. Meanwhile, in the financial markets, investors who invest in standard timer options may face the problems caused by the postponement of the exercising time for too low volatility, compared to standard vanilla options. In this regard, to overcome such disadvantages, we propose timer volatility-barrier options, which are activated and expired when the volatility arrives at a relatively low barrier level, with the original properties of the standard timer options. In this paper, by making use of the method of images, we derive an analytical formulas for the timer volatility-barrier options so that the volatility process can be driven by Heston stochastic volatility model, and verify the pricing accuracy of the timer options by comparing our solutions with those obtained from Monte Carlo simulations. Finally, we conduct numerical studies on the timer volatility-barrier options to examine the pricing sensitivities with respect to the various model parameters, and implement the discussion for pricing formula of double volatility barrier type of the timer options.

Multimodal photoacoustic microscopy and optical coherence tomography ocular biomarker imaging in Alzheimer's disease in mice

Alzheimer's disease (AD) is a neurodegenerative disease characterized by amyloid beta (Aβ)-containing extracellular plaques and tau-containing intracellular neurofibrillary tangles. Reliable and more accessible biomarkers along with associated imaging methods are essential for early diagnosis and to develop effective therapeutic interventions. Described here is an integrated photoacoustic microscopy (PAM) and optical coherence tomography (OCT) dual-modality imaging system for multiple ocular biomarker imaging in an AD mouse model. Anti-Aβ-conjugated Au nanochains (AuNCs) were engineered and administered to the mice to provide molecular contrast of Aβ. The retinal vasculature structure and Aβ deposition in AD mice and wild-type (WT) mice were imaged simultaneously by dual-wavelength PAM. OCT distinguished significant differences in retinal layer thickness between AD and WT animals. With the unique ability of imaging the multiple ocular biomarkers via a coaxial multimodality imaging system, the proposed system provides a new tool for investigating the progression of AD in animal models, which could contribute to preclinical studies of AD.

Informative value of diagnostic imaging methods in the detection of subepithelial formations of the stomach: a clinical case

INTRODUCTION: Subepithelial formations (SubEO) of the upper gastrointestinal tract (GI tract) are asymptomatic in most cases, and are a diagnostic finding in routine studies. Given the risk of malignancy of some SubEO, the question of treatment tactics remains relevant today: observation or surgical intervention.PURPOSE: To demonstrate diagnostic capabilities at the outpatient stage when choosing observational tactics in a patient with SubEO.MATERIALS AND METHODS: A dynamic observation of a 68-year-old woman with a randomly identified SubEO was demonstrated. The patient has been observed by a gastroenterologist since 2005 with a diagnosis of functional disorder of the gallbladder and sphincter of Oddi. In August 2022, when contacting the clinic with complaints of epigastric pain and heartburn, the following studies were performed: gastroscopy (FGDS), endoscopic ultrasound (EUS) with fine needle puncture, transabdominal ultrasound (TAUZI), computed tomography (CT) of the abdominal cavity, during which a submucosal formation of the stomach was diagnosed. The picture is more consistent with a gastrointestinal stromal tumor (GISO). Given the small size of the formation, the surveillance tactic was chosen. At the time of 12.2023, according to the results of control studies of the SubEO without dynamics.RESULTS: The follow-up period was 17 months, the results of the control studies justified the wait-and-see tactics and allowed avoiding unjustified surgical interventions and intraoperative complications.CONCLUSION: The combined use of TAUZI with endoscopy contributes to a better differential diagnosis and the choice of adequate treatment tactics.