Application Of Imaging Technology Research Articles

Application of imaging technology for the diagnosis of malignancy in the pancreaticobiliary duodenal junction (Review).

The pancreaticobiliary duodenal junction (PBDJ) is the connecting area of the pancreatic duct, bile duct and duodenum. In a broad sense, it refers to a region formed by the head of the pancreas, the pancreatic segment of the common bile duct and the intraduodenal segment, the descending and the horizontal part of the duodenum, and the soft tissue around the pancreatic head. In a narrow sense, it refers to the anatomical Vater ampulla. Due to its complex and variable anatomical features, and the diversity of pathological changes, it is challenging to make an early diagnosis of malignancy at the PBDJ and define the histological type. The unique anatomical structure of this area may be the basis for the occurrence of malignant tumors. Therefore, understanding and subclassifying the anatomical configuration of the PBDJ is of great significance for the prevention and treatment of malignant tumors at their source. The present review comprehensively discusses commonly used imaging techniques and other new technologies for diagnosing malignancy at the PBDJ, offering evidence for physicians and patients to select appropriate examination methods.

Vertical van der Waals Integrated p‐W0.09Re0.91S2 /GaN Heterojunction for Ultra‐High Detectivity UV Image Sensing

AbstractThe relentless advancement of van der Waals (vdW) heteroepitaxy technology has charted an expansive horizon for the integration and functionalization of heterogeneous materials. In this research, a 2D/3D vdW heterojunction photodetector based on p‐W0.09Re0.91S2/n‐GaN integrated on a Free‐standing (FS)‐GaN substrate, encompassing horizontal, quasi‐vertical, and vertical structures is have successfully fabricated. By incorporating a suite of performance enhancement strategies, including mixed‐dimensional stacking, p‐type doping, type‐II band alignment, and vertical structure design, the developed vertical structure photodetector has exhibited exceptional performance. Specifically, the detector achieves a high Responsivity of up to 497.60 A W−1, an impressive specific Detectivity of 8.41 × 1013 Jones, and a fast response speed (rise/decay time of 10 ms/20 ms). Additionally, the device is successfully applied in the realm of single‐pixel imaging, substantiating its potential for practical applications. The findings of this work not only signify notable strides in integration and optoelectronic performance within the optoelectronic device domain but also portend novel breakthroughs in imaging technology applications, bestowing renewed vigor and endless potential upon the evolution of this field.

Vehicle-Type Recognition Method for Images Based on Improved Faster R-CNN Model.

The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is of great significance for integrated traffic management. In this paper, an improved faster region with convolutional neural network features (Faster R-CNN) model was proposed for vehicle-type recognition. Firstly, the output features of different convolution layers were combined to improve the recognition accuracy. Then, the average precision (AP) of the recognition model was improved through the contextual features of the original image and the object bounding box optimization strategy. Finally, the comparison experiment used the vehicle image dataset of three vehicle types, including cars, sports utility vehicles (SUVs), and vans. The experimental results show that the improved recognition model can effectively identify vehicle types in the images. The AP of the three vehicle types is 83.2%, 79.2%, and 78.4%, respectively, and the mean average precision (mAP) is 1.7% higher than that of the traditional Faster R-CNN model.

Applications of Imaging Technologies in Fuchs Endothelial Corneal Dystrophy: A Narrative Literature Review.

Fuchs endothelial corneal dystrophy (FECD) is a complex genetic disorder characterized by the slow and progressive degeneration of corneal endothelial cells. Thus, it may result in corneal endothelial decompensation and irreversible corneal edema. Moreover, FECD is associated with alterations in all corneal layers, such as thickening of the Descemet membrane, stromal scarring, subepithelial fibrosis, and the formation of epithelial bullae. Hence, anterior segment imaging devices that enable precise measurement of functional and anatomical changes in the cornea are essential for the management of FECD. In this review, the authors will introduce studies on the application of various imaging modalities, such as anterior segment optical coherence tomography, Scheimpflug corneal tomography, specular microscopy, in vitro confocal microscopy, and retroillumination photography, in the diagnosis and monitoring of FECD and discuss the results of these studies. The application of novel technologies, including image processing technology and artificial intelligence, that are expected to further enhance the accuracy, precision, and speed of the imaging technologies will also be discussed.

Global trends in the application of fluorescence imaging in pancreatic diseases: a bibliometric and knowledge graph analysis.

In recent years, with the continuous development of fluorescence imaging technology, research on its application in pancreatic diseases has surged. This area is currently of high research interest and holds the potential to become a non-invasive and effective tool in the diagnosis and treatment of pancreatic diseases. The objective of this study is to explore the hotspots and trends in the field of fluorescence imaging technology applications in pancreatic diseases from 2003 to 2023 through bibliometric and visual analysis. This study utilized the Web of Science (core collection) to identify publications related to the application of fluorescence imaging technology in pancreatic diseases from 2003 to 2023. Tools such as CiteSpace (V 6.2.R6), VOSviewer (v1.6.20), and R Studio (Bibliometrix: R-tool version 4.1.4) were employed to analyze various dimensions including publication count, countries, institutions, journals, authors, co-cited references, keywords, burst words, and references. A comprehensive analysis was conducted on 913 papers published from January 1, 2003, to December 1, 2023, on the application of fluorescence imaging technology in pancreatic diseases. The number of publications in this field has rapidly increased, with the United States being the central hub. The University of California, San Diego emerged as the most active institution. "Biomaterials" was identified as the most influential journal. Authors with the most publications and the highest average citations per article are Hoffman, Robert M. and Luiken, George A., respectively. Keywords such as pancreatic cancer, cancer, expression, indocyanine green, and nanoparticles received widespread attention, with indocyanine green and nanoparticles being current active research hotspots in the field. This study is the first bibliometric analysis in the field of fluorescence imaging technology applications in pancreatic diseases. Our data will facilitate a better understanding of the developmental trends, identification of research hotspots, and direction in this field. The findings provide practical information for other scholars to grasp key directions and cutting-edge insights.

Optical Imaging Technology Application in Transmission Line Insulator Monitoring: A Review

Optical Imaging Technology Application in Transmission Line Insulator Monitoring: A Review

New technology and research progress in neurosurgery diagnosis and treatment

Neurosurgery is a vital branch of medicine that deals with the surgical management of brain disorders. The increasing demand for clinical solutions drives technological innovation, and the rapid progress in science and technology enables new discoveries, knowledge, techniques, and instruments in neurosurgery, expanding the scope and accuracy of diagnosis and treatment, and enhancing therapeutic outcomes. The author team combines domestic and international literature and previous clinical and scientific research experience, focusing on practical clinical problems in several subspecialties, including neuroimaging, neuronavigation and surgical robot assistance, central nervous system tumors, surgical treatment of cerebrovascular disease, functional neurosurgery, neuroinjury and neural repair, and digital neurosurgery. The paper summarizes in detail the research hotspots and puts forward the research direction prospects, including the innovative application of imaging technology, the development of fine surgery, the innovation of neuro-oncology diagnosis and treatment, the surgical standardization of cerebrovascular disease, the progress of neuromodulation, the individualized neurological alternative treatment and the digitalization of multi-dimensional information in neurosurgery.

Imaging Technologies for Cerebral Pharmacokinetic Studies: Progress and Perspectives.

Pharmacokinetic assessment of drug disposition processes in vivo is critical in predicting pharmacodynamics and toxicology to reduce the risk of inappropriate drug development. The blood–brain barrier (BBB), a special physiological structure in brain tissue, hinders the entry of targeted drugs into the central nervous system (CNS), making the drug concentrations in target tissue correlate poorly with the blood drug concentrations. Additionally, once non-CNS drugs act directly on the fragile and important brain tissue, they may produce extra-therapeutic effects that may impair CNS function. Thus, an intracerebral pharmacokinetic study was developed to reflect the disposition and course of action of drugs following intracerebral absorption. Through an increasing understanding of the fine structure in the brain and the rapid development of analytical techniques, cerebral pharmacokinetic techniques have developed into non-invasive imaging techniques. Through non-invasive imaging techniques, molecules can be tracked and visualized in the entire BBB, visualizing how they enter the BBB, allowing quantitative tools to be combined with the imaging system to derive reliable pharmacokinetic profiles. The advent of imaging-based pharmacokinetic techniques in the brain has made the field of intracerebral pharmacokinetics more complete and reliable, paving the way for elucidating the dynamics of drug action in the brain and predicting its course. The paper reviews the development and application of imaging technologies for cerebral pharmacokinetic study, represented by optical imaging, radiographic autoradiography, radionuclide imaging and mass spectrometry imaging, and objectively evaluates the advantages and limitations of these methods for predicting the pharmacodynamic and toxic effects of drugs in brain tissues.

Application of image technology to simulate optimal frequency of automatic collection of volumetric soil water content data

Volumetric soil water content (VSWC) monitoring is an important aspect of environmental monitoring of farmland. Accurate and real-time determination of the VSWC is important for crop drought stress diagnosis and smart irrigation. Image technology is commonly used in agricultural information technology. Based on image technology, we simulated the optimal frequency of the sensors to automatically collect VSWC data, thereby solving the problems of data redundancy and data analysis difficulties in real-time monitoring. In this study, a cotton field under mulched drip irrigation in 2018 was utilised as the research subject, 5TE sensors were arranged on the soil profile of the cotton field using the "grid method", and Voxler and Surfer software was used to model the VSWC data and draw contour maps. Image processing technology (image greyscale and image similarity comparison) was employed to determine the image algorithm suitable for contour map pre-processing and the best time period for VSWC monitoring. These results indicated that the contrast-limited adaptive histogram equalisation (CLAHE) greyscale algorithm is a suitable pre-processing algorithm for processing contour maps using image processing technology, and the best 5TE sensor data monitoring time period is every 10 h. This conclusion provides a theoretical reference for VSWC monitoring and water management in production.

Hyperspectral Imaging Analyses of Cleaning Tests on Edvard Munch's Monumental Aula Paintings

ABSTRACT The development of innovative applications of imaging technologies for monitoring change in cultural heritage objects is central to the CHANGE-ITN project. Within this framework, the authors’ ongoing work targets the harnessing of hyperspectral imaging (HSI) for the documentation of cleaning treatments on the monumental University of Oslo Aula unvarnished oil paintings on canvas (1909–1916) by Edvard Munch (1863–1944). This is applicable to unvarnished paint surfaces in general. Particularly, this paper evaluates HSI techniques for the investigation of cleaning tests carried out in 2008, which targeted the removal of visible, ingrained particulate matter from Munch's unvarnished oil paints and exposed grounds on Kjemi (1914–1916). An exploratory in situ HSI campaign using visible to near-infrared (VNIR) and short-wave infrared (SWIR) cameras was undertaken to capture the soiled or previously cleaned areas on the painting's surface, with comparisons to analogous mock-ups. Principal component analysis (PCA) was used to extract information about possible trends with respect to the soiling. Results indicate that in both VNIR and SWIR ranges, the intensity of reflectance can be used to discriminate statistically between soiled and unsoiled/cleaned areas, whereas in SWIR, combination and first overtone bands of oil peaks can also serve as markers.

Imaging Infection Across Scales of Size: From Whole Animals to Single Molecules

Infectious diseases are a leading cause of global morbidity and mortality, and the threat of infectious diseases to human health is steadily increasing as new diseases emerge, existing diseases reemerge, and antimicrobial resistance expands. The application of imaging technology to the study of infection biology has the potential to uncover new factors that are critical to the outcome of host-pathogen interactions and to lead to innovations in diagnosis and treatment of infectious diseases. This article reviews current and future opportunities for the application of imaging to the study of infectious diseases, with a particular focus on the power of imaging objects across a broad range of sizes to expand the utility of these approaches.

Application of image technology on pavement distress detection: A review

Digital image processing technology has been widely applied in various fields, and it is also increasingly used in pavement distress detection in recent years. The objective of this review article is to help researchers to select the most appropriate digital image processing technology (image acquisition equipment, processing, recognition technology and etc.) to study the pavement distress detection. Firstly, a proper application of the current image acquisition equipment is presented, and the advantages and disadvantages are compared. Secondly, the problems encountered in the practical application of image processing technology in pavement detection are presented. Further, the problems need to be solved in the future research are suggested, including cracks detection, pavement texture detection, temperature segregation detection, rutting detection, pothole detection, and joint faulting detection. In conclusion, the state of the art in pavement detection by digital image processing technology is summarized and proves that the digital image processing technology has become a promising method for pavement detection and materials analysis.

Bone metabolism and evolutionary origin of osteocytes: Novel application of FIB-SEM tomography.

Lacunae and canaliculi spaces of osteocytes are remarkably well preserved in fossilized bone and serve as an established proxy for bone cells. The earliest bone in the fossil record is acellular (anosteocytic), followed by cellular (osteocytic) bone in the jawless relatives of jawed vertebrates, the osteostracans, about 400 million years ago. Virtually nothing is known about the physiological pressures that would have initially favored osteocytic over anosteocytic bone. We apply focused ion beam-scanning electron microscopy tomography combined with machine learning for cell detection and segmentation to image fossil cell spaces. Novel three-dimensional high-resolution images reveal areas of low density around osteocyte lacunae and their canaliculi in osteostracan bone. This provides evidence for demineralization that would have occurred in vivo as part of osteocytic osteolysis, a mechanism of mineral homeostasis, supporting the hypothesis that a physiological demand for phosphorus was the principal driver in the initial evolution of osteocytic bone.

Deep learning-based approach using X-ray images for classifying Crambe abyssinica seed quality

The application of imaging technologies combined with state-of-the-art artificial intelligence techniques has provided important advances in the modern oilseed industry. Innovative tools have been designed to improve the characterization of different classes of seeds, and consequently, decision making has become more efficient. This study aimed to assess the potential of deep learning models based on convolutional neural networks (CNN) for monitoring the quality of crambe seeds using X-ray images. In the proposed approach, seeds with different physical and physiological attributes were used to create the models. The models achieved accuracies of 91, 95, and 82 % for discrimination of seeds based on the integrity of internal tissues, germination, and vigor, respectively. Therefore, our findings indicated that digital radiographic images are suitable to provide relevant information on the physical and physiological parameters of crambe seeds. Furthermore, the proposed methodology could be used to classify seeds quickly, non-destructively, and robustly.

OPPR: An Outsourcing Privacy-Preserving JPEG Image Retrieval Scheme with Local Histograms in Cloud Environment

As the wide application of imaging technology, the number of big image data which may containing private information is growing fast. Due to insufficient computing power and storage space for local server device, many people hand over these images to cloud servers for management. But actually, it is unsafe to store the images to the cloud, so encryption becomes a necessary step before uploading to reduce the risk of privacy leakage. However, it is not conducive to the efficient application of image, especially in the Content-Based Image Retrieval (CBIR) scheme. This paper proposes an outsourcing privacypreserving JPEG CBIR scheme. We design a set of JPEG format-compatible encryption method, making no file expansion to JPEG files. We firstly combine multiple adjacent 8 × 8 DCT coefficient blocks into big-blocks. Then, random scrambling and stream encryption are used on the binary code of DCT coefficients to protect the JPEG image privacy. The task of extracting features from encrypted images and retrieving similar images are done by the cloud server. The group index histograms of DCT coefficients are extracted from the encrypted big-blocks, then the global vector is produced to represent the JPEG image with the aid of bagof-words (BOW) model. The security analysis and experimental results show that our proposed scheme has strong security and good retrieval performance.

Radiology indispensable for tracking COVID-19

With the rapid spread of COVID-19 worldwide, early detection and efficient isolation of suspected patients are especially important to prevent the transmission. Although nucleic acid testing of SARS-CoV-2 is still the gold standard for diagnosis, there are well-recognized early-detection problems including time-consuming in the diagnosis process, noticeable false-negative rate in the early stage and lacking nucleic acid testing kits in some areas. Therefore, effective and rational applications of imaging technologies are critical in aiding the screen and helping the diagnosis of suspected patients. Currently, chest computed tomography is recommended as the first-line imaging test for detecting COVID-19 pneumonia, which could allow not only early detection of the typical chest manifestations, but also timely estimation of the disease severity and therapeutic effects. In addition, other radiological methods including chest X-ray, magnetic resonance imaging, and positron emission computed tomography also show significant advantages in the detection of COVID-19 pneumonia. This review summarizes the applications of radiology and nuclear medicine in detecting and diagnosing COVID-19. It highlights the importance for these technologies to curb the rapid transmission during the pandemic, considering findings from special groups such as children and pregnant women.

Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, pre-cooling, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins for multiple shipments in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables.

Research advances in imaging technology for food safety and quality control

Food quality and safety are issues of concern to the government, food industry, and consumers; hence, it is imperative to detect harmful substances in foodstuff. Traditional techniques for this purpose include biochemical methods and instrumental analysis methods such as chromatography and chromatography-mass spectrometry. These methods, however, are time-consuming and unable to obtain the spatial distribution of the analytes. Therefore, the development of rapid, non-destructive, real-time, and visual detection technologies has emerged as a hotspot in the field of food research. In recent years, hyperspectral imaging, which combines imaging and spectral technology, is rapidly gaining ground. This technique allows one to determine the geometrical characteristics and chemical composition of samples. Compared with traditional spectral technologies, hyperspectral imaging has the advantages of wide detection ranges, in addition to being real-time and non-destructive. At present, hyperspectral imaging is widely used in meat quality evaluation, detection of adulteration, and meat classification. In addition, Raman imaging is mainly used for the detection of illegal additives in food and for adulteration detection. This technology is fast, non-destructive, and low cost; furthermore, spectral and spatial information of the targets can be simultaneously obtained. Mass spectrometry imaging allows for the visualization and high-throughput analysis of sample tissues, without the need for complex sample preparation steps such as labeling and staining. Compared with other imaging technologies, mass spectrum information of substances can be obtained by mass spectrometry imaging. As a molecular visualized technology, it helps obtain the spatial distribution of nutrients and harmful substances in food. Mass spectrometry imaging has unique advantages in food research, e. g., it is used for molecular-level detection and accurate positioning of substances, and hence, it has excellent application prospects in this field. In this paper, recent literature data about imaging technologies in the field of food research, including 72 reports published in professional local and overseas magazines, are collated. The principles of hyperspectral imaging, Raman imaging, and mass spectrometry imaging are introduced, along with the detailed applications of these methods in the quality detection, source identification, and microbial pollution of food. In addition, it also includes food physical damage, food adulteration and food chemical residues. Besides, the advantages and disadvantages of these imaging technologies are discussed. Finally, prospects for the development of imaging technologies in food research are presented. Future work related to hyperspectral imaging should focus on the development of high-sensitivity cameras and high-resolution systems. Improving the data processing efficiency and adding prediction models are also key points for the future. Future studies on Raman imaging can focus on the application of different chemometrics algorithms that would improve the evaluation of food quality and safety parameters. Expanding the scope of application of these methods in food research will also be the focus of future research. Regarding mass spectrometry imaging, attempts should be made to improve the ionization methods, detection sensitivity, spatial resolution, and data processing effectiveness. Additionally, the combination of spectral imaging and mass spectrometry imaging gives full play to their advantages, so that spectral and mass spectrometry information of the targets can be obtained. In short, the application of imaging technologies in food research is expected to be more promising.

A novel intratumoral pH/redox-dual-responsive nanoplatform for cancer MR imaging and therapy

The integration of diagnostic and therapeutic functions in a nanoplatform has been a rapidly emerging method in the management of cancer. The application of imaging technology paves the way to track the pharmacokinetics of the nanoplatforms, to guide the treatment, and to monitor the therapeutic processes and outcomes. Herein, we reported a novel type of monodisperses mesoporous silica-coated superparamagnetic iron oxide-based multifunctional nanoplatform (DOX@MMSN-SS-PEI-cit) for the diagnosis and treatment of cancer. The fabrication process included the surface modification of monodisperses mesoporous silica nanoparticle (MMSN) with branched polyethyleneimine (PEI) via disulfide bonds and the further coupling of citraconic anhydride to PEI. Typically, the hydrolysis of amide bonds in the tumor microenvironment (TME) could lead to a negative-to-positive charge reversion, which can enhance the endosomal escape of the resulting nanoplatform. The rapid release of doxorubicin hydrochloride (DOX) directly killed the cancer cells. Due to the superparamagnetic iron oxide-based high-resolution T2-weighted MR imaging contrast agents, this novel multifunctional nanoplatform successfully realized MR imaging, targeted drug delivery and controlled release in one system, and achieved significant improvement in tumor diagnosis and therapy. In summary, the therapeutic nanoplatform is a promising option in precise cancer treatment.

Recent fluorescence imaging technology applications of indocyanine green in general thoracic surgery.

Thoracic surgeons perform a wide variety of cancer operations, which are often associated with high morbidity and mortality. Thus, thoracic surgery involves many special challenges that require innovative solutions. The increased utilization of minimally invasive practices, poor overall cancer survival, and significant morbidity of critical operations remain key obstacles to overcome. Fluorescence imaging technology (FIT), involving the implementation of fluorescent dyes and imaging systems, is currently used as an adjunct for general thoracic surgery in many situations and includes sentinel lymph node mapping, pulmonary intersegmental plane identification, pulmonary nodule identification, pulmonary bullous lesion detection, evaluation of the anastomotic perfusion after tracheal surgery, and thoracic duct imaging for postoperative chylothorax. This technology enhances the surgeon's ability to perform operations, and has specific advantages. We review some of the key studies that demonstrate the applications of FIT in the field of general thoracic surgery, focusing on the use of indocyanine green.