Images Of Objects Research Articles

Analysis of the Current Status and Means of Studying Gravitational Lensing

Lens in physics is a kind of mirror that deflects light and results in a deflated and distorted image of the light-emitting object. Hence, the phenomenon of a massive celestial object deflecting light to form a distorted, magnified image of a luminous object is well known as gravitational lensing. In this paper, a brief summary of the concept and the status quo of the research and methodology related to it is rendered. This study offers a review of the overall founding history of the concept, and presents the current advances in relevant research. Later, the principles of generation and construction modeling of the concept, basic methodology are demonstrated. Subsequently, the applications of gravitational lensing in dark matter research, celestial body relationship research and validation of research results will be illustrated with examples of recent research. Then, the potential discrepancies in the use of gravitational lensing and future prospects are discussed in short. These results intended to help scholars who lack an understanding of the concept to have a primer on the field of gravitational lensing.

Object color knowledge representation occurs in the macaque brain despite the absence of a developed language system.

Animals guide their behaviors through internal representations of the world in the brain. We aimed to understand how the macaque brain stores such general world knowledge, focusing on object color knowledge. Three functional magnetic resonance imaging (fMRI) experiments were conducted in macaque monkeys: viewing chromatic and achromatic gratings, viewing grayscale images of their familiar fruits and vegetables (e.g., grayscale strawberry), and viewing true- and false-colored objects (e.g., red strawberry and green strawberry). We observed robust object knowledge representations in the color patches, especially the one located around TEO: the activity patterns could classify grayscale pictures of objects based on their memory color and response patterns in these regions could translate between chromatic grating viewing and grayscale object viewing (e.g., red grating-grayscale images of strawberry), such that classifiers trained by viewing chromatic gratings could successfully classify grayscale object images according to their memory colors. Our results showed direct positive evidence of object color memory in macaque monkeys. These results indicate the perceptually grounded knowledge representation as a conservative memory mechanism and open a new avenue to study this particular (semantic) memory representation with macaque models.

Feasibility of ultra-low volume contrast pulmonary arterial injection protocol for CTA in TAVI candidates with severe renal failure

Abstract Introduction Computer tomography angiography (CTA) has an important role in patient screening before TAVI. The high volumes of iodinated contrast media (CM) injected during the examination represents the main limitation of CTA with a dose-dependent association with contrast-induced nephropathy. Purpose Show the feasibility of an ultra-low-dose CM pulmonary arterial injection protocol for CTA in TAVI candidates in high risk patients with severe renal failure. Methods In our retrospective study seventy-seven (77) patients with CKD on stage 3b or worse (eGFR ≤ 45/min/1.73 m2) underwent pre-procedural CTA for TAVI-planning with a pulmonary arterial administration of CM. All CTA examinations were performed using a Dual Source 256-slice CT system. All enhanced CT acquisitions were performed using our dedicated biphasic injection protocol composed of two connected phases of contrast medium injection using different amount of CM (8, 10, 15 and 20 ml) according to BMI (<22 kg/m2; 22-24 kg/m2; 24-28 kg/m2; >28 kg/m2). The CM was administered directly into the pulmonary artery in all patients. For each examination was measured Intraluminal attenuation at thoraco-abdominal aorta and along the ilio-femoral vessels using the ROI (mean SD in HU) as objective image parameters (Picture 1). Serum creatinine and the eGFR were recorded pre-TAVI CT scan and after the procedures as reference of CM to renal function. Results CTA was successfully performed in all patients. Mean creatinine was 2.25 ± 0,76 mg/dL and mean GFR was 26.5± 8.1 mL/min/m2. Mean vessel attenuation was 352±164 HU at the aortic annulus, 238.1 ± 141,3 HU in the aortic arch and 266±168 in the iliac arteries (Picture 2). The mean image noise was 28.2 ± 7 HU in the aortic annulus, 24.4 ± 10,9 in the right external iliac artery, and 25. ± 12,8 HU in the left external iliac artery. After CTA examination, a total of 74 patients (96%) underwent TAVI. Mean CM dose administered during TAVI procedure was 150,6 ± 85,8. Three patients (3.9%) were abolished for TAVI. Postprocedural settings complications were recorded. A total of 31 patients (41%) had grade 1 paravalvular regurgitation, 10 patients (14%) grade 2. No cases of valve embolization or anulus rupture occured. No major vascular complications occured. No significant differences between creatine and eGFR pre and post procedures (p<0,001). Conclusions The use of an ultra-low-dose CM pulmonary arterial injection protocol for CTA in TAVI candidates does not affect the renal function and procedural success rate is high and the clinical outcomes after TAVI is satisfactory according to the litterature.

Pediatric contrast-enhanced chest CT on a photon-counting detector CT: radiation dose and image quality compared to energy-integrated detector CT.

Photon counting detector (PCD) CT benefits from reduced noise compared with conventional energy-integrating detector (EID) CT, which should translate to improved image quality and reduced radiation exposure for pediatric patients undergoing chest CT with IV contrast. To determine the differences in radiation exposure and image quality of PCD CT and EID CT in pediatric chest CT with intravenous (IV) contrast. In this institutional review board-approved retrospective observational study, 20 scan pairs (20 PCD CT; 20 EID CT) for children who underwent chest CT with IV contrast on both a PCD CT (Siemens NAEOTOM Alpha) and an EID CT (Siemens SOMATOM Definition Edge or Force) within 12months were reviewed independently by three pediatric radiologists for three subjective quality features on 5-point Likert scales: overall quality, small structure delineation, and motion artifact. Objective measures of image quality (image noise, signal-to-noise ratio, and contrast-to-noise ratio) were assessed by a single radiologist in several locations in the chest through region of interest measurement of Hounsfield units (HU) and standard deviation. Patient-related and radiation exposure parameters were collected for each scan and summarized with median and interquartile range (IQR). The Wilcoxon rank-sum test was utilized to compare groups. A P < 0.05 indicated statistical significance. Inter-observer agreement of subjective image quality metrics was analyzed using weighted kappa. Age (14.2years vs 13.8years, P= 0.15), height (P= 0.13), weight (P= 0.21), and BMI (P = 0.24) did not significantly differ between groups. There were 10 male and 3 female patients. Compared to EID CT, PCD CT showed lower radiation exposure parameters including volumetric CT dose index, 1.7mGy (IQR 1.1-2.4mGy) vs 3.8mGy (IQR 2.0-4.7mGy) (P< 0.01), and size-specific dose estimate, 2.6mGy (IQR 1.8-3.1mGy) vs 5.0mGy (IQR 3.3-6.2mGy) (P< 0.01). Objective image quality of lung parenchyma was improved on the PCD CT scanner, including image noise 119.5 HU (IQR 95.4-135.7 HU) vs 143.1 HU (IQR 125.4-169.8 HU) (P < 0.01), signal-to-noise ratio (SNR) -6.1 (IQR -8.4 to -4.8) vs -4.9 (IQR -5.6 to -3.8) (P= 0.01), and contrast-to-noise ratio -63.9 (-84.1 to -57.5) vs -60.5 (-76.3 to -52.5) (P = 0.01). Motion artifact was improved on the PCD CT scanner (P< 0.01). No significant differences in overall image quality or small structure delineation were identified (P= 0.06 and P= 0.31). PCD CT pediatric chest CT had significantly reduced radiation exposure, improved image quality, and reduced motion artifact compared with EID CT.

CT coronary fractional flow reserve based on artificial intelligence using different software: a repeatability study

ObjectiveThis study aims to assess the consistency of various CT-FFR software, to determine the reliability of current CT-FFR software, and to measure relevant influence factors. The goal is to build a solid foundation of enhanced workflow and technical principles that will ultimately improve the accuracy of measurements of coronary blood flow reserve fractions. This improvement is critical for assessing the level of ischemia in patients with coronary heart disease.Methods103 participants were chosen for a prospective research using coronary computed tomography angiography (CCTA) assessment. Heart rate, heart rate variability, subjective picture quality, objective image quality, vascular shifting length, and other factors were assessed. CT-FFR software including K software and S software are used for CT-FFR calculations. The consistency of the two software is assessed using paired-sample t-tests and Bland-Altman plots. The error classification effect is used to construct the receiver operating characteristic curve.ResultsThe CT-FFR measurements differed significantly between the K and S software, with a statistical significance of P < 0.05. In the Bland-Altman plot, 6% of the points (14 out of 216) fell outside the 95% consistency level. Single-factor analysis revealed that heart rate variability, vascular dislocation offset distance, subjective image quality, and lumen diameter significantly influenced the discrepancies in CT-FFR measurements between two software programs (P < 0.05). The ROC curve shows the highest AUC for the vessel shifting length, with an optimal cut-off of 0.85 mm.ConclusionCT-FFR measurements vary among software from different manufacturers, leading to potential misclassification of qualitative diagnostics. Vessel shifting length, subjective image quality score, HRv, and lumen diameter impacted the measurement stability of various software.

Instance segmentation models for detecting floating macroplastic debris from river surface images

Quantifying the transport of floating macroplastic debris (FMPD) in waterways is essential for understanding the plastic emission from land. However, no robust tool has been developed to monitor FMPD. Here, to detect FMPD on river surfaces, we developed five instance segmentation models based on state-of-the-art You Only Look Once (YOLOv8) architecture using 7,356 training images collected via fixed-camera monitoring of seven rivers. Our models could detect FMPD using object detection and image segmentation approaches with accuracies similar to those of the pretrained YOLOv8 model. Our model performances were tested using 3,802 images generated from 107 frames obtained by a novel camera system embedded in an ultrasonic water level gauge (WLGCAM) installed in three rivers. Interestingly, the model with intermediate weight parameters most accurately detected FMPD, whereas the model with the most parameters exhibited poor performance due to overfitting. Additionally, we assessed the dependence of the detection performance on the ground sampling distance (GSD) and found that a smaller GSD for image segmentation approach and larger GSD for object detection approach are capable of accurately detecting FMPD. Based on the results from our study, more appropriate category selections need to be determined to improve the model performance and reduce the number of false positives. Our study can aid in the development of guidelines for monitoring FMPD and the establishment of an algorithm for quantifying the transport of FMPD.

Fourier phase retrieval using physics-enhanced deep learning

Fourier phase retrieval (FPR) aims to reconstruct an object image from the magnitude of its Fourier transform. Despite its widespread utility in various fields of engineering and science, the inherent ill-posed nature of the FPR problem poses a significant challenge. Here we propose a learning-based approach that incorporates the physical model of the FPR imaging system with a deep neural network. Our method includes two steps: First, we leverage the image formation model of the FPR to guide the generation of data for network training in a self-supervised manner. Second, we exploit the physical model to fine-tune the pre-trained model to impose the physics-consistency constraint on the network prediction. This allows us to integrate both implicit prior from training data and explicit prior from the physics of the imaging system to address the FPR problem. Simulation and experiments demonstrate that the proposed method is accurate and stable, showcasing its potential for wide application in fields utilizing the FPR. We have made our source code available for non-commercial use.

Correlation between Lund-Mackay computed tomography score and subjective symptoms in Chinese patients with nasal polyps.

The association between objective imaging findings and subjective symptoms remains undefined. To investigate the correlation between objective imaging findings and symptom severity in the overall chronic rhinosinusitis with nasal polyps (CRSwNP) population as well as its subendotypes according to the eosinophilic inflammation. Patients with CRSwNP undergoing endoscopic sinus surgery were included. All participants completed the Sino-Nasal Outcome Test (SNOT-22) questionnaire. The Lund-Mackay (LM) CT scores was also obtained. Spearman correlation analysis was performed to evaluate the correlation between CT scores and SNOT-22 scores. Forty-four non-eosinophilic CRSwNP(neCRSwNP) and 93 eosinophilic CRSwNP (eCRSwNP) patients were recruited. There was significant association between LM total score and nasal subdomain of SNOT-22 in the overall CRSwNP patients. The nasal symptom of SNOT-22 was significantly associated with maxillary, frontal, sphenoid, anterior drainage, and posterior drainage in all CRSwNP patients. In eCRSwNP and neCRSwNP patients, the sense of smell/taste was associated with sphenoid and posterior drainage. Thick nasal discharge was positively correlated with maxillary, frontal, and anterior drainage only in eCRSwNP patients. The high score of sphenoid and posterior drainage may suggest the occurrence of loss of sense of smell/taste, while high score of maxillary sinus may indicate suffering from thick nasal discharge in eCRSwNP patients.

Phase-Sensitive Fluorescence Image Correlation Spectroscopy.

Fluorescence lifetime imaging microscopy is sensitive to molecular interactions and environments. In homo-dyne frequency-domain fluorescence lifetime imaging microscopy, images of fluorescence objects are acquired at different phase settings of the detector. The detected intensity as a function of detector phase is a sinusoidal function that is sensitive to the lifetime of the fluorescent species. In this paper, the theory of phase-sensitive fluorescence image correlation spectroscopy is described. In this version of lifetime imaging, image correlation spectroscopy analysis (i.e., spatial autocorrelation) is applied to successive fluorescence images acquired at different phase settings of the detector. Simulations of different types of lifetime distributions reveal that the phase-dependent density of fluorescent objects is dependent on the heterogeneity of lifetimes present in the objects. We provide an example of this analysis workflow to a cervical cancer cell stained with a fluorescent membrane probe.

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).

Significant feature suppression and cross-feature fusion networks for fine-grained visual classification

The technique of extracting different distinguishing features by locating different part regions to achieve fine-grained visual classification (FGVC) has made significant improvements. Utilizing attention mechanisms for feature extraction has become one of the mainstream methods in computer vision, but these methods have certain limitations. They typically focus on the most discriminative regions and directly combine the features of these parts, neglecting other less prominent yet still discriminative regions. Additionally, these methods may not fully explore the intrinsic connections between higher-order and lower-order features to optimize model classification performance. By considering the potential relationships between different higher-order feature representations in the object image, we can enable the integrated higher-order features to contribute more significantly to the model’s classification decision-making capabilities. To this end, we propose a saliency feature suppression and cross-feature fusion network model (SFSCF-Net) to explore the interaction learning between different higher-order feature representations. These include (1) an object-level image generator (OIG): the intersection of the output feature maps of the last two convolutional blocks of the backbone network is used as an object mask and mapped to the original image for cropping to obtain an object-level image, which can effectively reduce the interference caused by complex backgrounds. (2) A saliency feature suppression module (SFSM): the most distinguishing part of the object image is obtained by a feature extractor, and the part is masked by a two-dimensional suppression method, which improves the accuracy of feature suppression. (3) A cross-feature fusion method (CFM) based on inter-layer interaction: the output feature maps of different network layers are interactively integrated to obtain high-dimensional features, and then the high-dimensional features are channel compressed to obtain the inter-layer interaction feature representation, which enriches the output feature semantic information. The proposed SFSCF-Net can be trained end-to-end and achieves state-of-the-art or competitive results on four FGVC benchmark datasets.

Intelligent Vision System with Pruning and Web Interface for Real-Time Defect Detection on African Plum Surfaces

Agriculture stands as the cornerstone of Africa’s economy, supporting over 60% of the continent’s labor force. Despite its significance, the quality assessment of agricultural products remains a challenging task, particularly at a large scale, consuming valuable time and resources. The African plum is an agricultural fruit that is widely consumed across West and Central Africa but remains underrepresented in AI research. In this paper, we collected a dataset of 2892 African plum samples from fields in Cameroon representing the first dataset of its kind for training AI models. The dataset contains images of plums annotated with quality grades. We then trained and evaluated various state-of-the-art object detection and image classification models, including YOLOv5, YOLOv8, YOLOv9, Fast R-CNN, Mask R-CNN, VGG-16, DenseNet-121, MobileNet, and ResNet, on this African plum dataset. Our experimentation resulted in mean average precision scores ranging from 88.2% to 89.9% and accuracies between 86% and 91% for the object detection models and the classification models, respectively. We then performed model pruning to reduce model sizes while preserving performance, achieving up to 93.6% mean average precision and 99.09% accuracy after pruning YOLOv5, YOLOv8 and ResNet by 10–30%. We deployed the high-performing YOLOv8 system in a web application, offering an accessible AI-based quality assessment tool tailored for African plums. To the best of our knowledge, this represents the first such solution for assessing this underrepresented fruit, empowering farmers with efficient tools. Our approach integrates agriculture and AI to fill a key gap.

Salient object detection with bayesian inference based on radar and camera fusion used in UAV obstacle avoidance

Most existing salient object detection methods are sensitive to background noise and rely on prior information in UAV obstacle avoidance applications despite detection methods witnessing rapid progress. In this paper, we propose an efficient framework for salient object detection based on radar-camera fusion and iterative Bayesian optimization. A rough salient object (RSO) image is first built through radar and camera calibration. Next, the RSO image is used to calculate the edge response based on the receptive field mechanism of the primary visual cortex to construct the contour image. Finally, the above two images and the 2D Gaussian distribution are jointly integrated into an iterative Bayesian optimization scheme to get the final salient object image. Different from typical detection methods, this method suppresses background noise by filtering out irrelevant pixels using fusion information. The Bayesian inference framework’s detection performance is improved by precise spatial prior, consisting of optimized contour and RSO images. Experimental results indicate that the presented algorithm performs well against state-of-the-art salient object detection methods on several reference datasets in different evaluation metrics.

The value of amide proton transfer imaging in assessing brain damage in patients with obstructive sleep apnea

ObjectiveTo investigate the application value of amide proton transfer (APT) magnetic resonance imaging in evaluating brain damage in patients with obstructive sleep apnea (OSA). Materials and methods49 OSA patients and 25 healthy individuals matched for age and sex were included as case and control groups. All study participants underwent conventional 3D T1WI and APT imaging of the head. The APT values were measured in each of the brain regions of interest (ROI). To compare the differences in APT values of each brain region between the case and control groups, to compare the differences in the efficacy of APT values of each brain region in diagnosing OSA, and to investigate the correlation between APT values of each brain region and Apnea Hypopnea Index (AHI) and minimum blood oxygen saturation. ResultsCompared to the control group, patients with OSA had elevated APT values in several brain regions (P < 0.05), and the diagnostic efficacy of the combined diagnosis of OSA by multiple brain regions is better than that by each brain region alone.Left frontal APT values were positively correlated with AHI in the case group (r = 0.33, P = 0.020).In the case group, the APT values of bilateral frontal lobe, parietal lobe, occipital lobe, left temporal lobe, right thalamus, left internal capsule, left lenticular nucleus, right cerebellar hemisphere and splenium of corpus callosum were negatively correlated with the minimum blood oxygen saturation, and the APT values for each of the above ROIs were positively correlated. ConclusionAPT imaging has a certain value in evaluating brain damage in OSA patients, which may provide a new objective imaging basis for the clinical diagnosis of OSA.

Real-Time Nonlinear Image Reconstruction in Electrical Capacitance Tomography Using the Generative Adversarial Network

This study investigated the potential of the generative adversarial neural network (cGAN) image reconstruction in industrial electrical capacitance tomography. The image reconstruction quality was examined using image patterns typical for a two-phase flow. The training dataset was prepared by generating images of random test objects and simulating the corresponding capacitance measurements. Numerical simulations were performed using the ECTsim toolkit for MATLAB. A cylindrical sixteen-electrode ECT sensor was used in the experiments. Real measurements were obtained using the EVT4 data acquisition system. The reconstructed images were evaluated using selected image quality metrics. The results obtained using cGAN are better than those obtained using the Landweber iteration and simplified Levenberg–Marquardt algorithm. The suggested method offers a promising solution for a fast reconstruction algorithm suitable for real-time monitoring and the control of a two-phase flow using ECT.

Exploring the combined impact of color and editing on emotional perception in authentic films: Insights from behavioral and neuroimaging experiments

Film color and editing are crucial elements in filmmaking, posing significant questions about their effective use in eliciting emotional responses from viewers. While prior research has explored the impacts of color and editing independently, their combined effect remains largely unexplored, leaving the open question of how these elements together affect emotional perception. This study investigates the combined impact of film color and editing on emotion perception, utilizing both subjective behavioral scales and objective functional magnetic resonance imaging (fMRI) techniques. It employs two experiments with a two-factor design to assess the interactions between film color (colored and black-and-white) and film editing (fearful, neutral, and happy levels) on emotional perceptions. Under the direction of a professional filmmaker, a series of film sequences was created for six experimental conditions. In Experiment 1, 117 participants watched the film sequences and subjectively rated the emotional valence of the neutral face in the sequence, uncovering a significant interaction effect between film color and editing on the valence rating of the neutral face. In Experiment 2, 67 participants watched similar film sequences in an MRI scanner to analyze their brain activation patterns. Distinct activations were identified in regions including the insula, anterior cingulate cortices (ACC), and middle frontal gyrus, where a region-of-interest (ROI) analysis of the left ACC revealed an interaction effect on neural responses. These results underscore the integral role of color and editing in influencing viewers’ emotion perception using two types of measurements. This research provides novel insights into the behavioral and neural responses to filmmaking elements that underpin film reception and offers practical implications for filmmakers, encouraging a more holistic approach to considering color and editing to further enrich the audience’s emotional experiences at the pre-production stage of filmmaking.

Enhancing dynamic target reconstruction and tracking based on ghost imaging and deep convolutional neural networks

Ghost imaging requires a large amount of sampling data, which limits its applications in the study of dynamic objects. Here, we propose an imaging technique based on deep convolutional neural networks (SaDunet) that can be used to examine the dynamics of target objects. By replacing the traditional correlation imaging reconstruction approach with SaDunet, the ability to recover high-quality images at low sampling rates is enhanced. The motion process of the target object is decomposed into multiple motion frames, and then each frame is imaged separately. Experiments show that the reconstructed image of the target object obtained by this scheme is of high quality, contains almost no noise, and accurately reflects the motion behavior of the target object.

Dynamic digital image correlation method for rolling convective contact

Digital image correlation (DIC) is an increasingly popular and effective non-contact method for measuring full-field displacements and strains of deformable bodies under load. Current DIC methods applied to bodies undergoing large displacements and rotations require a large measurement area for both the reference (i.e., undeformed) image and the deformed images. This can limit the resulting resolution of the displacement and strain fields. To address this issue, we propose a two-stage dynamic DIC method capable of measuring displacements and strains under material convection with high resolution. During the first stage, the reference image is assembled from smaller, high-resolution images of the undeformed object obtained using a spatially-fixed or moving frame. Following capture, each sub-image is rigidly translated and rotated into its appropriate place, thereby producing a full, high-resolution image of the reference body. In stage two, images of the loaded and deformed body, again obtained using a small camera frame with high resolution, are aligned with matching regions of the undeformed composite image using BRISK feature detection before performing DIC. We demonstrate the method on a contact problem whereby an elastomeric roller travels along a rigid surface. In doing so, we obtain fine-resolution measurements of the state of strain of the region of the roller sidewall in contact with the substrate, even as new material convects through the region of interest. We present these measurements as a series of images and videos capturing strain evolution as the roller transitions from static loads to a fully dynamic steady-state, documenting the effectiveness of the method.

The combination of 18F-fluorodeoxyglucose and 18F 9-fluoropropyl-(+)-dihydrotetrabenazine positron emission tomography for distinguishing between early-onset and late-onset idiopathic Parkinson disease and analyzing influencing factors.

The classification of Parkinson disease by age of onset has proven to be a valuable method for subtyping, given its practical application in clinical settings. However, the interactions between the metabolic brain changes, dopaminergic dysfunction, and clinical manifestations in patients with early-onset (early-iPD) and late-onset (late-iPD) idiopathic Parkinson disease have not been adequately evaluated. Therefore, this study aimed to investigate the difference in cerebral metabolism and presynaptic dopaminergic function between patients with early-iPD and those with late-onset disease using 18F-fluorodeoxyglucose (18F-FDG) and [18F] 9-fluoropropyl-(+)-dihydrotetrabenazine (18F-FP-DTBZ) positron emission tomography (PET). Furthermore, the goal was to further explore the correlation between imaging measurements and clinical manifestations in the early and late idiopathic patients with Parkinson disease. This cross-sectional study included 80 patients with idiopathic Parkinson disease and 29 healthy control participants who underwent 18F-FDG and18F-FP-DTBZ PET imaging at Xuanwu Hospital, Capital Medical University from August 2022 to August 2023. The patients were categorized into early-iPD (n=27) and late-iPD (n=53) groups based on an age threshold of 50 years. The mean standardized uptake value of 18F-FDG and the standardized uptake value ratio (SUVR) of 18F-FP-DTBZ were compared between the early-iPD and late-iPD groups using unpaired Student t-tests. Furthermore, pairwise correlations among cerebral metabolism, dopaminergic function, and corresponding clinical ratings in all patients were conducted using Pearson correlation analysis. Patients with late-iPD exhibited a significant metabolic decrease in the frontal, parietal, and temporal cortex, along with the globus pallidus, putamen, thalamus, and cerebellum, compared to those with early-iPD in 18F-FDG PET imaging (all P values <0.05). Furthermore, the 18F-FP-DTBZ binding potential was significantly lower in the contralateral caudate and anterior putamen of patients with late-iPD compared to those with early-iPD (contralateral caudate: 3.16±1.2 vs. 2.63±0.7, P=0.020; contralateral anterior putamen: 2.49±1.2 vs. 2.05±0.7, P=0.040). Further analysis of the correlations between imaging clinical features revealed that glucose metabolism increases and dopaminergic function decreases with higher motor ratings. 18F-FDG and 18F-FP-DTBZ PET offer an objective molecular imaging basis for distinguishing between early-onset and late-onset idiopathic with Parkinson disease. Additionally, correlation analysis between imaging and clinical data represents a new approach for exploring the potential applications in future studies involving patients with early-iPD and late-iPD.

SCIENTIFIC AND TECHNICAL SUBSTANTIATION OF THE PARAMETERS OF THE RADIOLOCATION DEVICE FOR THE DETECTION OF PROHIBITED ITEMS

The paper presents a scientific and technical substantiation of the parameters of the radar device, which allows to detect and visualize forbidden objects by means of electromagnetic radiation of the microwave range. A model of a multistatic scheme for measuring waves reflected from an object based on the holographic method is developed. A software complex of simulation modeling of radar device operation has been developed, by means of which reconstructions of object images for different configurations of sparse sensor subsystems of the radar device have been obtained. An optimal configuration of the sparse sensor subsystem was selected by the method of peak signal-to-noise ratio analysis, for which a multi static hologram was modeled and a reconstruction of the metal rod image was generated. The reconstruction of the metal rod image obtained as a result of simulation modeling has a recognizable contour, which confirms the prospect of developing an experimental prototype of a radar device for detecting prohibited objects.