Imaging Technology Research Articles

An Examination of Computer Graphics and Image Technology’s Role in the Components of Visual Communication Design

An Examination of Computer Graphics and Image Technology’s Role in the Components of Visual Communication Design

An improved attentive residue multi-dilated network for thermal noise removal in magnetic resonance images

Magnetic resonance imaging (MRI) technology is crucial in the medical field, but the thermal noise in the reconstructed MR images may interfere with the clinical diagnosis. Removing the thermal noise in MR images mainly contains two challenges. First, thermal noise in an MR image obeys Rician distribution, where the statistical features are not consistent in different regions of the image. In this case, conventional denoising methods like spatial convolutional filtering will not be appropriate to deal with it. Second, details and edge information in the image may get damaged while smoothing the noise. This paper proposes a novel deep-learning model to denoise MR images. First, the model learns a binary mask to separate the background and signal regions of the noised image, making the noise left in the signal region obey a unified statistical distribution. Second, the model is designed as an attentive residual multi-dilated network (ARM-Net), composed of a multi-branch structure, and supplemented with a frequency-domain-optimizable discrete cosine transform module. In this way, the deep-learning model will be more effective in removing the noise while maintaining the details of the original image. Furthermore, we have also made improvements on the original ARM-Net baseline to establish a new model called ARM-Net v2, which is more efficient and effective. Experimental results illustrate that over the BraTS 2018 dataset, our method achieves the PSNR of 39.7087 and 32.6005 at noise levels of 5% and 20%, which realizes the state-of-the-art performance among existing MR image denoising methods.

A Review of Five Existing Hornet-Tracking Methods.

Hornet is a general term for insects of the genus Vespa (Hymenoptera: Vespidae). Hornets are predatory insects distributed worldwide. They often appear at apiaries in groups to prey on honey bees, and cause incalculable losses in the honey bee industry. In the face of hornet intrusion, tracking a homing hornet to find its nest is the most efficient way to discover and eliminate the hornets around an apiary. Here, five hornet-tracking methods (hornet tag tracking, triangulation, thermal imaging technology, harmonic radar, and radio telemetry) are reviewed. The advantages, disadvantages and feasibility of each method are discussed to improve the strategies for tracking hornets. Therefore, this review provides ideas for the development of hornet-tracking technology and for improving honey bee protection.

Application of green building thermal energy conservation based on light imaging equipment in landscape optimization of waterfront space

As the global focus on sustainable development intensifies, green buildings have become an important way to reduce energy consumption and improve environmental quality. The purpose of this study is to explore the application of technology based on optical imaging equipment in the heat saving of green buildings, especially in the landscape optimization of waterfront space, and evaluate its effectiveness and feasibility. In this study, light imaging equipment was used to monitor the heat distribution of different green buildings in the waterfront space, and combined with environmental data and energy consumption, the influence of architectural design and landscape configuration on heat energy conservation was analyzed. Through case study and quantitative analysis, the optimization effect of different design schemes on thermal effect is evaluated. The study found that rational allocation of buildings and surrounding landscape can significantly reduce heat loss, reflecting the importance of heat management. Light imaging technology effectively reveals the thermal energy dynamics of buildings under different weather conditions, verifying the potential of green buildings to reduce energy consumption. Therefore, the research based on optical imaging equipment provides a new perspective and method for the energy saving of green buildings in the waterfront space, and demonstrates the important role of optimizing landscape design in improving building energy efficiency.

ISK: Index steering kernel for multi-SWIR-image super-resolution

Multi-image super-resolution (SR) techniques reconstruct high-resolution (HR) images from multiple low-resolution (LR) counterparts, yet existing methods often fall short in reproducing high-frequency details when compared to true HR images, especially short-wave infrared (SWIR) images. This discrepancy, coupled with the technological and cost limitations of enhancing high-resolution imaging system performance through lens aperture size and sensor density, impedes the advancement of imaging systems and digital image technology. Addressing this, we propose the Index Steering Kernel (ISK) method, an innovation grounded in Gaussian process multi-image super-resolution, equivariant kernel theory, and steering kernel methods. Validated through comparative resolution chart tests using a SWIR camera (InGaAs detector), our method achieves a TV Line resolution of 60lp/mm in 4-image 2 × SR experiments, closely approaching the 65lp/mm of genuine high-resolution SWIR images and surpassing alternative approaches. In 9-image 3 × SR experiments, ISK consistently outperforms comparative methods. As a non-data-driven approach, ISK incorporates data compression designs, enabling 3 × SR of nine 640 × 512 SWIR images on a single NVIDIA GTX GeForce 1080Ti graphics card without image splitting, proving its computational efficiency while maintaining superior SR quality.

(Invited) Surface State Modulation of Colloidal Quantum Dots and Construction of Quantum Dot Based Infrared Detectors

Near-infrared photodetection and imaging devices play an important role in fields such as biological detection, information communication, military meteorology, etc. Traditional imaging devices require integration with readout circuits, and the complex integration process limits the development of infrared imaging systems. Lead sulfide colloidal quantum dots have excellent photoelectric properties in the infrared region, with a tunable bandgap width ranging from 0.4 to 1.5 eV, and spectral response covering below 2400 nm. Lead sulfide quantum dot films exhibit high absorption coefficients in the infrared region and excellent carrier mobility. In recent years, the performance of infrared detectors based on quantum dot systems has rapidly improved, and they are expected to develop into a new type of infrared detection and imaging technology. The speaker's team has combined infrared quantum dots with visible light quantum dots to prepare a simple, low-cost, and efficient infrared upconversion imaging device. By using the method of inducing band bending through interface electron storage, the efficiency of carrier tunneling and photon conversion has been improved, and the application of this technology in biological imaging has been preliminarily demonstrated. This report will introduce the development of quantum dot infrared detection materials and devices in recent years, as well as the research progress of the speaker's team in this area.

(Invited) Developing Optical Nanosensors for Biomedical Applications

Novel biomedical application technologies have been rapidly improved in the past few decades. Despite recent technological progress, significant challenges persist toward more accessible, precise, inexpensive, rapid, automated, and patient-facing methods. Optical spectroscopic and imaging technologies have multiple advantages to current practices, such as versatility, fast and robust signal, and noninvasive and nondestructive probing techniques that can be easily integrated into current medical devices such as catheters, endoscopes, or biopsy needle channels. Single-walled carbon nanotubes (SWCNTs) have been developed as near-infrared (NIR) fluorescent nanosensors and demonstrated in vitro and in vivo with great interest for using them in biological and clinical applications and the methods designed to probe them. The advantages of SWCNT include resistance to photobleaching and high sensitivity towards biological environments.Gynecologic cancers are challenging to diagnose. Patient prognosis and quality of life are affected substantially by this problem. In this talk, I will present recent work on developing new technologies to improve gynecological cancer detection and grant new research tools for understanding fundamental mechanisms in cancer development using liquid biopsy and in vitro and in vivo sensor approaches. With artificial intelligence algorithms, we harness SWCNTs' unique optical properties and sensitivity to develop intelligent optical sensors. We developed platforms to detect multiple gynecological cancer biomarkers as implantable devices in patient biofluids and the uterine cavity. Applying machine learning algorithms to analyze the optical response of the sensors enabled the precise detection of multiplexed biomarkers. In addition, when implanted in human uteri, the sensors detected the biomarkers and successfully differentiated between benign and malignant cases. These technologies will significantly improve diagnostics and cancer research, leading to robust, point-of-care technologies for early-stage diagnosis.

(Invited) Nanohybrid Carbon Nanotube and P3HT Composite Semiconductor Materials for Solution Processed Optoelectronics

Carbon nanotube networks have demonstrated promise for enabling printable transistors which can be utilized as switching elements within active matrices. Such active matrices have been utilized for controlling pixels and interrogating sensor elements. A new promising technology for imaging, called an active pixel matrix, aims to integrate both the underlying transistor and sensor element within a single device, namely a phototransistor. Carbon nanotubes are poised to enable printable active pixel matrices for sensing applications, however, due to minimal absorption a secondary material needs to be added as a photo synthesizer. Additionally, the material must be sufficiently electrically coupled with the carbon nanotube network to allow for efficient carrier extraction. Our lab has previously developed techniques for crystallizing P3HT directly onto carbon nanotubes using solution-phase soft epitaxy. In this work, the solution phased technique has been adapted to functional carbon nanotube networks already deposited onto a surface.I will present the performance of a device that consists of a solution-processed carbon nanotube network, which acts as the carrier extraction layer, and a surface crystalized P3HT, which acts as the absorption element. First, I will detail the fabrication scheme and soft-epitaxial method of polymer crystallization. Next, I will overview how the P3HT crystallization affects the electronic performance of the devices, including the photo response. The device exhibits high responsivity, with a maximum responsivity value of 3 x 105. Additionally, the device exhibits a wide spectral response within the visible range, due to the exciton generation at the P3HT/CNT interface. I will also present the transient response of the device, which provides insight into the role of carrier injection/trapped states within the material.Lastly, I will discuss the potential applications for an active matrix of phototransistors, including neuromorphic computing. In these specific applications, the semiconductor can be utilized as the functional material within an Organic Electrochemical Transistor (OECT), where it exhibits behavior associated with mixed ionic/electronic conduction (MEIC). I will benchmark the performance, and discuss various methods to fabricated charge trapped switches, a key memory/computational element for future neuromorphic architectures.

The desmosome comes into focus.

The desmosome is a cell-cell adhesive junction that provides integrity and mechanical resistance to tissues through its attachment to the intermediate filament cytoskeleton. Defects in desmosomes cause diseases impacting the heart, epidermis, and other epithelia. In this review, we provide a historical perspective on the discovery of the desmosome and how the evolution of cellular imaging technologies revealed insights into desmosome structure and function. We also discuss recent findings using contemporary imaging approaches that have informed the molecular order, three-dimensional architecture, and associations of desmosomes with organelles such as the endoplasmic reticulum. Finally, we provide an updated model of desmosome molecular organization and speculate upon novel functions of this cell junction as a signaling center for sensing mechanical and other forms of cell stress.

Determination of malathion content in sorghum grains using hyperspectral imaging technology combined with stacked machine learning models

The rapid and precise detection of pesticide residues remains a pressing safety issue in the food industry. In this study, a rapid method for analyzing pesticide residues in sorghum was developed, combining hyperspectral imaging (HSI) technology with stacking ensemble learning (SEL) models. The HSI spectral data were preprocessed using the multivariate scatter correction (MSC) algorithm. The gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and categorical boosting (CatBoost) algorithms were employed to identify the feature wavelengths with high contributions to the predictive model, and the performances of SEL, GBDT, XGBoost, LGBM, and CatBoost to accurately predict the pesticide residues in sorghum samples were compared. The SEL model constructed using the characteristic wavelength selected by CatBoost has the best predictive performance, with RMSEP, RP2, and RPD values of 0.6940 mg/kg, 0.9798, and 7.029, respectively. The study demonstrated that the combination of HSI and SEL enabled the accurate analysis of pesticide residues in sorghum, providing a reference for the utilization of HSI methods to accurately measure the concentrations of pesticide residues in sorghum and other food products.

Hyperspectral image reconstruction for predicting chick embryo mortality towards advancing egg and hatchery industry

As the demand for food surges and the agricultural sector undergoes a transformative shift towards sustainability and efficiency, the need for precise and proactive measures to ensure the health and welfare of livestock becomes paramount. In the egg and hatchery industry, hyperspectral imaging (HSI) has emerged as a cutting-edge, non-destructive technique for fast and accurate egg quality analysis, including detecting chick embryo mortality. However, the high cost and operational complexity compared to conventional RGB imaging are significant bottlenecks in the widespread adoption of HSI technology. To overcome these hurdles and unlock the full potential of HSI, a promising solution is hyperspectral image reconstruction from standard RGB images. This study aims to reconstruct hyperspectral images from RGB images for non-destructive early prediction of chick embryo mortality. Initially, the performance of different image reconstruction algorithms, such as HRNET, MST++, Restormer, and EDSR were compared to reconstruct the hyperspectral images of the eggs in the early incubation period. Later, the reconstructed spectra were used to differentiate live from dead embryos eggs using the XGBoost and Random Forest classification methods. Among the reconstruction methods, HRNET showed impressive reconstruction performance with MRAE of 0.0955, RMSE of 0.0159, and PSNR of 36.79 dB. This study motivated the idea that harnessing imaging technology integrated with smart sensors and data analytics has the potential to improve automation, enhance biosecurity, and optimize resource management towards sustainable agriculture 4.0.

Image acquisition technology for unmanned aerial vehicles based on YOLO - Illustrated by the case of wind turbine blade inspection

Wind energy, as a renewable energy source, is becoming increasingly important. The maintenance and damage detection of wind turbine blades are particularly crucial. For this purpose, the study aims to optimize the You Only Look Once (YOLO) processing algorithm for drone images to improve the detection efficiency. Firstly, the damage images captured by drones are preprocessed and optimized, including deblurring, noise reduction, and image enhancement. Subsequently, the YOLOv5 model is improved in terms of structure and regression function, and a novel damage detection model is proposed. The research results indicated that the minimum loss function value of the improved model was 2.75, the average accuracy was 95 %, and the highest intersection over union was 91 %. After simulation testing, the detection effect of this model on abrasion, crackle, edge cracking, and coating peeling images was significantly better than other models in the same series. Its average time was as short as 2.43 s, reaching a maximum frame rate of 35.46. From this, the combination of drone image technology and improved image processing algorithm has a positive impact on improving the operational efficiency and safety of wind turbine blades. Compared with the traditional methods, the proposed model has significant advantages in terms of accuracy and real-time performance of damage detection, providing a new technical means for efficient maintenance of wind turbines. Meanwhile, the method shows high robustness and reliability in different types of damage detection, demonstrating the extensive potential in practical applications.

Usefulness of combined pseudo-continuous arterial spin labelling and spectroscopic analysis in schizophrenic Egyptian population sample

BackgroundSchizophrenia is a prevalent psychiatric disorder that affects 1% of the global population. Schizophrenia frequently begins in late adolescence or early adulthood, causing significant educational, social, and economic costs for people and society. Functional neuroimaging research on schizophrenia physiopathology has been beneficial. Arterial spin labelling (ASL) is one of functional magnetic resonance imaging (fMRI) technologies that assess brain function without radiation. ASL uses magnetic resonance (MR) imaging to quantify tissue-level brain perfusion non-invasively. Arterial spin labelling (ASL) is one of the functional magnetic resonance imaging (fMRI) technologies that assess the brain function without radiation hazards. ASL uses magnetic resonance (MR) imaging to quantify tissue-level brain perfusion non-invasively. Many publications were performed on role of different advanced MRI techniques in schizophrenia, but our study insisted on the added value of combined ASL and MRS over the conventional MRI in schizophrenic Egyptian population sample.Aim of the workThe purpose of this work was to evaluate the added value of combined ASL-perfusion MRI and MRS in schizophrenic patients.MethodsThis prospective case–control study was carried out on two groups: First group was 30 patients who were diagnosed clinically as schizophrenic patients, and second group was 20 healthy volunteers as a control group for comparison in the period from August 2021 till July 2022.ResultsThe majority of newly diagnosed cases had significant higher positive symptoms than chronic cases. According to arterial spin labelling (ASL) data, rCBF was noticed to be reduced in anterior cingulate, frontal lobe, and parietal lobe of both patients’ subgroups but more significant in chronically ill patients. Convergent results of decreased rCBF were also found in the parietal lobe and occipital lobe. Magnetic resonance spectroscopic analysis showed that NAA was decreased in the anterior cingulate cortex, thalami and basal ganglia of the newly diagnosed cases more than chronic cases. The ASL-MRI perfusion accurately detected the hypo-perfusion of different brain regions with sensitivity 100%, specificity 66.67%, positive predictive value 96.43%, negative predictive value 100%, and accuracy 96.67%, while MR spectroscopy showed sensitivity 100%, specificity 33.33%, positive predictive value 93.10%, negative predictive value 100%, and accuracy 93.33% in evaluation of changes of brain metabolites.ConclusionASL is a promising functional MRI technique that can produce together with MRS quantitative information about the metabolites of different brain regions. The ASL-MRI appears as a reliable non-invasive technique to measure cerebral blood flow and identify decreased rCBF without any contrast administration, and it could be repeatable which helps in early diagnosis as well as follow-up of the progression of the disease.

Intracranial meningioma: A review of recent and emerging data on the utility of preoperative imaging for management.

Meningiomas are the most common neoplasms of the central nervous system, accounting for approximately 40% of all brain tumors. Surgical resection represents the mainstay of management for symptomatic lesions. Preoperative planning is largely informed by neuroimaging, which allows for evaluation of anatomy, degree of parenchymal invasion, and extent of peritumoral edema. Recent advances in imaging technology have expanded the purview of neuroradiologists, who play an increasingly important role in meningioma diagnosis and management. Tumor vascularity can now be determined using arterial spin labeling and dynamic susceptibility contrast-enhanced sequences, allowing the neurosurgeon or neurointerventionalist to assess patient candidacy for preoperative embolization. Meningioma consistency can be inferred based on signal intensity; emerging machine learning technologies may soon allow radiologists to predict consistency long before the patient enters the operating room. Perfusion imaging coupled with magnetic resonance spectroscopy can be used to distinguish meningiomas from malignant meningioma mimics. In this comprehensive review, we describe key features of meningiomas that can be established through neuroimaging, including size, location, vascularity, consistency, and, in some cases, histologic grade. We also summarize the role of advanced imaging techniques, including magnetic resonance perfusion and spectroscopy, for the preoperative evaluation of meningiomas. In addition, we describe the potential impact of emerging technologies, such as artificial intelligence and machine learning, on meningioma diagnosis and management. A strong foundation of knowledge in the latest meningioma imaging techniques will allow the neuroradiologist to help optimize preoperative planning and improve patient outcomes.

Recent Advancements in Ultrasound Contrast Agents Based on Nanomaterials for Imaging.

Ultrasound (US) is a type of mechanical wave that is capable of transmitting energy through biological tissues. By utilization of various frequencies and intensities, it can elicit specific biological effects. US imaging (USI) technology has been continuously developed with the advantages of safety and the absence of radiation. The advancement of nanotechnology has led to the utilization of various nanomaterials composed of both organic and inorganic compounds as ultrasound contrast agents (UCAs). These UCAs enhance USI, enabling real-time monitoring, diagnosis, and treatment of diseases, thereby facilitating the widespread adoption of UCAs in precision medicine. In this review, we introduce various UCAs based on nanomaterials for USI. Their principles can be roughly divided into the following categories: carrying and transporting gases, endogenous gas production, and the structural characteristics of the nanomaterial itself. Furthermore, the synergistic benefits of US in conjunction with various imaging modalities and their combined application in disease monitoring and diagnosis are introduced. In addition, the challenges and prospects for the development of UCAs are also discussed.

Labeling selectivity of lipid droplets fluorescent probes: Twisted intramolecular charge transfer (TICT) vs intramolecular charge transfer (ICT)

Fluorescence imaging technology is a versatile and essential tool in the field of biomedical research. To obtain excellent imaging results, the precise labeling of fluorescent probes is an important prerequisite. Nevertheless, the labeling selectivity of most fluorescent probes is not satisfactory, new design concepts are desperately needed. In this context, two isomeric lipid droplets (LDs) fluorescent probes Lipi-Cz-1 and Lipi-Cz-2 have been sophisticatedly developed with TICT and ICT-emitting characteristic, respectively. The more environmentally sensitive TICT-emitting Lipi-Cz-1 exhibits a significantly enhanced labeling selectivity in LDs imaging compared to the ICT-emitting Lipi-Cz-2, sufficiently illustrating the effectiveness of TICT-emitting characteristic in improving labeling selectivity. Additionally, Lipi-Cz-1 displays high photostability and biocompatibility. These advantages enable Lipi-Cz-1 to be finely applied in multimode fluorescence imaging, e.g. time-lapse 3D confocal imaging to monitor changes of the number and size of LDs during starvation, two-photon 3D imaging to compare the variations of LDs in various liver tissues, and STED super-resolution imaging to visualize the nanoscale LDs with the resolution of 65 nm. Overall, these imaging findings validate the effectiveness of the new strategy for improving the labeling selectivity.

Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning

Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible through medical imaging. Effective classification of these cancers is critical for accurate diagnosis and treatment. This study addresses critical challenges in the diagnostic imaging of lung and colon cancers, which are among the leading causes of cancer-related deaths worldwide. Recognizing the limitations of existing diagnostic methods, which often suffer from overfitting and poor generalizability, our research introduces a novel deep learning framework that synergistically combines the Xception and MobileNet architectures. This innovative ensemble model aims to enhance feature extraction, improve model robustness, and reduce overfitting.Our methodology involves training the hybrid model on a comprehensive dataset of histopathological images, followed by validation against a balanced test set. The results demonstrate an impressive classification accuracy of 99.44%, with perfect precision and recall in identifying certain cancerous and non-cancerous tissues, marking a significant improvement over traditional approach.The practical implications of these findings are profound. By integrating Gradient-weighted Class Activation Mapping (Grad-CAM), the model offers enhanced interpretability, allowing clinicians to visualize the diagnostic reasoning process. This transparency is vital for clinical acceptance and enables more personalized, accurate treatment planning. Our study not only pushes the boundaries of medical imaging technology but also sets the stage for future research aimed at expanding these techniques to other types of cancer diagnostics.

Polyfunctional Arylamine Based Nanofiltration Membranes with Enhanced Aggressive Organic Solvents Resistance.

Organic solvent nanofiltration (OSN) membranes with high separation performance and excellent stability in aggressive organic solvents are urgently desired for chemical separation. Herein, we utilized a polyfunctional arylamine tetra-(4-aminophenyl) ethylene (TAPE) to prepare a highly cross-linked polyamide membrane with a low molecular weight cut-off (MWCO) of 312 Da. Owing to its propeller-like conformation, TAPE formed micropores within the polyamide membrane and provided fast solvent transport channels. Importantly, the rigid conjugated skeleton and high connectivity between micropores effectively prevented the expansion of the polyamide matrix in aggressive organic solvents. The membrane maintained high separation performance even immersed in N,N-dimethylformamide for 90 days. Based on the aggregation-induced emission (AIE) effect of TAPE, the formation of polyamide membrane can be visually monitored by fluorescence imaging technology, which achieved visual guidance for membrane fabrication. This work provides a vital foundation for utilizing polyfunctional monomers in the interfacial polymerization reaction to prepare high-performance OSN membranes.

The Role of Cone Beam Computed Tomography (CBCT) in the Diagnosis and Clinical Management of Medication-Related Osteonecrosis of the Jaw (MRONJ).

Medication-related osteonecrosis of the jaw (MRONJ) is a debilitating condition associated with antiresorptive and antiangiogenic medications that are frequently used in treating osteoporosis and cancers. With the ability to produce high-resolution images with a lower radiation dose, cone beam computed tomography (CBCT) is an emerging technology in maxillofacial imaging that offers several advantages in evaluating MRONJ. This review aims to summarise the radiological features of MRONJ as observed via CBCT and highlight its advantages over two-dimensional plain films in assessing MRONJ. CBCT has the capability to detect early MRONJ lesions, characterise the extent and nature of lesions, distinguish MRONJ from other osseous pathologies, and assist in treatment planning. By leveraging the advantages of CBCT, clinicians can enhance their understanding of MRONJ, improve decision making, and ultimately optimize patient care.

Miniaturized Head-Mount Doppler Optical Coherence Tomography Scope for Freely Moving Mouse.

This study presents a miniaturized head-mount optical coherence tomography (OCT) system tailored for high-resolution brain imaging in freely moving mice, providing an advanced noninvasive imaging tool in neuroscience research. Leveraging optical coherence tomography technology, the system enables depth-resolved imaging and integrates functional OCT extensions, including angiography and Doppler imaging. Remarkably lightweight at 1.5 g, the device allows for the preservation of natural mouse behavior during imaging sessions. With a maximum 4 × 4 mm field of view and 7.4 μm axial resolution, the system offers reliable imaging capabilities. Noteworthy features include focal adjustability, rotary joint integration for fiber-twist-free operation, and a high-speed swept-source OCT laser at 200 kHz, facilitating real-time imaging. By providing insights into brain mechanisms and neurological disorders without disrupting natural behavior, this innovative system holds promise as a powerful tool in neuroscience research. Its compact design and comprehensive imaging capabilities make it well-suited for studying various brain regions and dynamic processes, contributing significantly to our understanding of brain function and pathology.