Complete Image Research Articles

MPVF: 4D Medical Image Inpainting by Multi-Pyramid Voxel Flows.

Generatinga detailed 4D medical image usually accompanies with prolonged examination time and increased radiation exposure risk. Modern deep learning solutions have exploited interpolation mechanisms to generate a complete 4D image with fewer 3D volumes. However, existing solutions focus more on 2D-slice information, thus missing the changes on the z-axis. This article tackles the 4D cardiac and lung image interpolation problem by synthesizing 3D volumes directly. Although heart and lung only account for a fraction of chest, they constantly undergo periodical motions of varying magnitudes in contrast to the rest of the chest volume, which is more stationary. This poses big challenges to existing models. In order to handle various magnitudes of motions, we propose a Multi-Pyramid Voxel Flows (MPVF) model that takes multiple multi-scale voxel flows into account. This renders our generation network rich information during interpolation, both globally and regionally. Focusing on periodic medical imaging, MPVF takes the maximal and the minimal phases of an organ motion cycle as inputs and can restore a 3D volume at any time point in between. MPVF is featured by a Bilateral Voxel Flow (BVF) module for generating multi-pyramid voxel flows in an unsupervised manner and a Pyramid Fusion (PyFu) module for fusing multiple pyramids of 3D volumes. The model is validated to outperform the state-of-the-art model in several indices with significantly less synthesis time.

Synaptic density is correlated with Aβ plaques and neurofibrillary tau tangles across the clinical and biologic Alzheimer’s disease continuum

AbstractBackgroundThe extent to which amyloid‐ß burden and neurofibrillary tau tangle burden are associated with synaptic degeneration in vivo is not well known. PET imaging was used to examine the cross‐sectional relationship between neurofibrillary tau tangle burden and amyloid‐ß plaque burden with synaptic density among individuals who spanned the clinical and biologic Alzheimer’s disease continuum.MethodParticipants were recruited from the Wisconsin Alzheimer’s Disease Research Center and the Wisconsin Registry for Alzheimer’s Prevention study. N = 64 participants were included in the analysis based on completed cognitive testing, structural MR and PET imaging using [11C]PiB DVR for amyloid burden, [18F]MK‐6240 SUVR for regional neurofibrillary tau burden, and [11C]UCB‐J DVR for regional synaptic density burden analysis. Amyloid burden was indexed as a global [11C]PiB DVR representing the mean of brain regions that typically accumulate amyloid. UCB‐J DVR and MK‐6240 SUVR regions of interest were selected based on the histopathological staging of tau pathology in Alzheimer’s disease with a focus on regions of tau pathology described by earlier and later Braak stages. The first composite region of interest (ROI1) comprised the hippocampus and entorhinal cortex, and the second (ROI2) comprised the amygdala, fusiform, lingual gyrus, and the inferior temporal gyrus. Linear correlations (Pearson’s r) tested the relationship between UCB‐J DVR and PiB DVR, as well as UCB‐J DVR and MK‐6240 SUVR.ResultGlobal [11C]PiB DVR showed a moderate negative relationship with UCB‐J DVR in ROI1 (r = ‐0.42, p = 0.0006) and a weak relationship with UCB‐J DVR in ROI2 (r = ‐0.33, p = 009). [18F]MK‐6240 SUVR and UCB‐J DVR were negatively correlated in ROI1 (r = ‐0.44, p = 0.0003) and in ROI2 (r = ‐0.42, p = 0.005).ConclusionBoth amyloid burden and tau pathology were associated with lower synaptic density. Tau and synaptic density appeared to show moderate correlations among brain regions involved in both earlier and more advanced tau pathology, whereas amyloid burden appeared to be more strongly related to synaptic density in brain regions where more advanced disease is typically observed.

Learning Unified Hyper-Network for Multi-Modal MR Image Synthesis and Tumor Segmentation With Missing Modalities.

Accurate segmentation of brain tumors is of critical importance in clinical assessment and treatment planning, which requires multiple MR modalities providing complementary information. However, due to practical limits, one or more modalities may be missing in real scenarios. To tackle this problem, existing methods need to train multiple networks or a unified but fixed network for various possible missing modality cases, which leads to high computational burdens or sub-optimal performance. In this paper, we propose a unified and adaptive multi-modal MR image synthesis method, and further apply it to tumor segmentation with missing modalities. Based on the decomposition of multi-modal MR images into common and modality-specific features, we design a shared hyper-encoder for embedding each available modality into the feature space, a graph-attention-based fusion block to aggregate the features of available modalities to the fused features, and a shared hyper-decoder for image reconstruction. We also propose an adversarial common feature constraint to enforce the fused features to be in a common space. As for missing modality segmentation, we first conduct the feature-level and image-level completion using our synthesis method and then segment the tumors based on the completed MR images together with the extracted common features. Moreover, we design a hypernet-based modulation module to adaptively utilize the real and synthetic modalities. Experimental results suggest that our method can not only synthesize reasonable multi-modal MR images, but also achieve state-of-the-art performance on brain tumor segmentation with missing modalities.

Glenoid segmentation from computed tomography scans based on a 2-stage deep learning model for glenoid bone loss evaluation.

The best-fitting circle drawn by computed tomography (CT) reconstruction of the en face view of the glenoid bone to measure the bone defect is widely used in clinical application. However, there are still some limitations in practical application, which can prevent the achievement of accurate measurements. This study aimed to accurately and automatically segment the glenoid from CT scans based on a 2-stage deep learning model and to quantitatively measure the glenoid bone defect. Patients who were referred to our institution between June 2018 and February 2022 were retrospectively reviewed. The dislocation group consisted of 237 patients with a history of ≥2 unilateral shoulder dislocations within 2 years. The control group consisted of 248 individuals with no history of shoulder dislocation, shoulder developmental deformity, or other disease that may lead to abnormal morphology of the glenoid. All patients underwent CT examination with a 1-mm slice thickness and a 1-mm increment, including complete imaging of the bilateral glenoid. A residual neural network (ResNet) location model and a U-Net bone segmentation model were constructed to develop an automated segmentation model for the glenoid from CT scans. The data set was randomly divided into training (201 of 248) and test (47 of 248) data sets of control-group data and training (190 of 237) and test (47 of 237) data sets of dislocation-group data. The accuracy of the stage 1 (glenoid location) model, the mean intersection-over-union value of the stage 2 (glenoid segmentation) model, and the glenoid volume error were used to assess the performance of the model. The R2 value and Lin concordance correlation coefficient were used to assess the correlation between the prediction and the gold standard. A total of 73,805 images were obtained after the labeling process, and each image was composed of CT images of the glenoid and its corresponding mask. The average overall accuracy of stage 1 was 99.28%; the average mean intersection-over-union value of stage 2 was 0.96. The average glenoid volume error between the predicted and true values was 9.33%. The R2 values of the predicted and true values of glenoid volume and glenoid bone loss (GBL) were 0.87 and 0.91, respectively. The Lin concordance correlation coefficient value of the predicted and true values of glenoid volume and GBL were 0.93 and 0.95, respectively. The 2-stage model in this study showed a good performance in glenoid bone segmentation from CT scans and could quantitatively measure GBL, providing a data reference for subsequent clinical treatment.

Gray Matter Thickness and Subcortical Nuclear Volume in Men After SARS-CoV-2 Omicron Infection

The clinical manifestations and effects on the brain of the SARS-CoV-2 Omicron variant in the acute postinfection phase remain unclear. To investigate the pathophysiological mechanisms underlying clinical symptoms and changes to gray matter and subcortical nuclei among male patients after Omicron infection and to provide an imaging basis for early detection and intervention. In this cohort study, a total of 207 men underwent health screening magnetic resonance imaging scans between August 28 and September 18, 2022; among them, 98 provided complete imaging and neuropsychiatric data. Sixty-one participants with Omicron infection were reevaluated after infection (January 6 to 14, 2023). Neuropsychiatric data, clinical symptoms, and magnetic resonance imaging data were collected in the acute post-Omicron period, and their clinical symptoms were followed up after 3 months. Gray matter indexes and subcortical nuclear volumes were analyzed. Associations between changes in gray matter and neuropsychiatric data were evaluated with correlation analyses. Gray matter thickness and subcortical nuclear volume change data were compared before and after Omicron infection. The gray matter indexes and subcutaneous nuclear volume were generated from the 3-dimensional magnetization-prepared rapid acquisition gradient echo and were calculated with imaging software. Ninety-eight men underwent complete baseline data collection; of these, 61 (mean [SD] age, 43.1 [9.9] years) voluntarily enrolled in post-Omicron follow-up and 17 (mean [SD] age, 43.5 [10.0] years) voluntarily enrolled in 3-month follow-up. Compared with pre-Omicron measures, Beck Anxiety Inventory scores were significantly increased (median, 4.50 [IQR, 1.00-7.00] to 4.00 [IQR, 2.00-9.75]; P = .006) and depressive distress scores were significantly decreased (median, 18.00 [IQR, 16.00-20.22] to 16.00 [IQR, 15.00-19.00]; P = .003) at the acute post-Omicron follow-up. Fever, headache, fatigue, myalgia, cough, and dyspnea were the main symptoms during the post-Omicron follow-up; among the participants in the 3-month follow-up, fever (11 [64.7%] vs 2 [11.8%]; P = .01), myalgia (10 [58.8%] vs 3 (17.6%]; P = .04), and cough (12 [70.6%] vs 4 [23.5%]; P = .02) were significantly improved. The gray matter thickness in the left precuneus (mean [SD], 2.7 [0.3] to 2.6 [0.2] mm; P < .001) and right lateral occipital region (mean [SD], 2.8 [0.2] to 2.7 [0.2] and 2.5 [0.2] to 2.5 [0.2] mm; P < .001 for both) and the ratio of the right hippocampus volume to the total intracranial volume (mean [SD]. 0.003 [0.0003] to 0.003 [0.0002]; P = .04) were significantly reduced in the post-Omicron follow-up. The febrile group had reduced sulcus depth of the right inferior parietal region compared with the nonfebrile group (mean [SD], 3.9 [2.3] to 4.8 [1.1]; P = .048. In the post-Omicron period, the thickness of the left precuneus was negatively correlated with the Beck Anxiety Inventory scores (r = -0.39; P = .002; false discovery rate P = .02), and the ratio of the right hippocampus to the total intracranial volume was positively correlated with the Word Fluency Test scores (r = 0.34; P = .007). In this cohort study of male patients infected with the Omicron variant, the duration of symptoms in multiple systems after infection was short. Changes in gray matter thickness and subcortical nuclear volume injury were observed in the post-Omicron period. These findings provide new insights into the emotional and cognitive mechanisms of an Omicron infection, demonstrate its association with alterations to the nervous system, and verify an imaging basis for early detection and intervention of neurological sequelae.

Hemoglobin Genotype, Hemolytic Activity, Fetal Hemoglobin and Retinal Changes on Optic Coherence Tomography in Children with Sickle Cell Disease

Introduction: Sickle cell disease (SCD) affects multiple organs, including the eye. Optical coherence tomography (OCT) is a quick, noncontact test that provides high-resolution images of distinctive retinal layers. OCT has revolutionized ophthalmology examination and offers valuable new information on retinal damage in SCD. Our earlier studies show that OCT has a higher detection rate for retinal changes in SCD, offering earlier diagnosis than fundoscopy alone. Additionally, abnormal retinal OCT correlates with cerebrovascular disease (CVD) strongly suggesting that retinal OCT may aid in detecting and monitoring SCD related brain injury. One of the characteristics of SCD is the marked variability in patients' clinical manifestations. SCD genotype impacts the presentation of sickle cell retinopathy (SCR). Though OCT has revealed a higher frequency of SCR and more extensive retinal changes associated with the more severe SCD genotypes SS and Sβ0 as compared to SC, significant heterogeneity remains even among patients with the same genotype. Early identification of factors predictive of organ damage, such as SCR and CVD, will allow initiation of individualized disease-modifying treatment before organ systems suffer repeated hypoxic-ischemic events, thus improving long-term outcomes. Degree of hemolysis and fetal hemoglobin (FHb) are among the most important factors affecting SCD severity. There have been limited studies on their effect on ocular health in children with SCD. Methods: The Delaware Comprehensive Sickle Cell Research Center performed a prospective longitudinal cohort study in which all participants underwent complete ophthalmologic examination and OCT imaging. Posterior pole volume scans centered at approximately 3 mm temporal to the foveal were obtained. Areas of retinal thinning were identified using the thickness map. Medical history, brain imaging and laboratory data were collected. All available FHb values were recorded to examine the changing trend with age. Retinal findings were correlated with brain imaging and clinical data. Analysis was performed using IBM SPSS Statistics 27 (IBM, Armonk, NY). The Chi-square test was used for categorical data, and the nonparametric test for continuous variable analysis. The relationship between FHb, age at FHb testing and OCT was examined using multiple regression analysis. Results: From July 2015 to April 2023, 181 (88 males) patients aged 5.35 - 20.39 (12.72 ± 4.29) years with SCD (108 SS, 56 SC, 10 Sβ+ thalassemia, 5 Sβ0 thalassemia and 2 other) enrolled in the study. Visual acuity ranged 20/20 - 20/40. On funduscopic exams, 23 of 181 (12.71%) showed signs of SCR, whereas 107 (59.12%) had retinal injuries on OCT. Seventy-six patients with SS/Sβ0 genotype had brain MRI, and 28 had abnormal findings (Table). Twenty-four of the 28 patients (85.71%) also had abnormal OCT (p = 0.021). Children with SC and SS/Sβ0 disease with normal retinal OCT had FHb values significantly higher than those with abnormal OCT (Z =-2.61 and -4.16, p = 0.009 and 0.000). Children with SS/Sβ0 disease, abnormal OCT had more elevated reticulocyte counts than those with normal OCT (Z = -2.848, p = 0.004). SS genotype were evaluated for FHb values. As expected, FHb levels decline with age, and the rate of descent diminishes (i.e., more rapid fall at an early age). Interestingly, the decrease in FHb is significantly more profound before age 15 for patients with abnormal OCT than those with normal OCT (Figure). The effect of hydroxyurea (HU) in preventing SCR is inconclusive. Children with normal OCT had similar FHb levels without and with HU. However, HU raises FHb levels among those with abnormal OCT. Conclusions: 1) Retinal and brain injury correlates in SS/Sβ0, and OCT may help to select patients for MRI. 2) Abnormal OCT is associated with increased reticulocyte count, indicating heightened hemolysis. While AST and indirect bilirubin levels are not significantly different between normal and abnormal OCT groups in the current dataset, we will repeat the analysis of these hemolytic markers with a larger sample. 3) Early serial measurement of FHb levels throughout childhood can identify patients at high risk for subsequent severe complications such as SCR and CVD who might benefit from early disease modifying treatments. These important findings and the impact of hydroxyurea require further study in a larger patient population undergoing serial exams.

Sex difference in body image, exercise motivation and social comparison among Instagram users: a cross sectional study

In the 21st century, impact of social media, particularly Social Networking Sites (SNSs) has been linked to a wide range of human beliefs and expectations. Growing body of research has indicated that body image concerns along with exercise motivation and social comparison are on the rise among young adults. The present study aimed to examine the sex difference in body image, exercise motivation and social comparison among people who use Instagram in the age group 20-30. A total of 212 participants (men=106, women=106) aged 20-30 years, who are users of Instagram completed Body Self Image Questionnaire Short Form as a measure of Body image, Exercise Motivation Inventory – 2 as a measure of Exercise Motivation and Instagram as a Tool for Social Comparison as a measure of Social Comparison. Results showed that a significant difference in body image exist across gender with body image issues higher among females and significant difference in exercise motivation across gender with exercise motivation higher among males. No sex differences were seen in social comparison. It was concluded that body image concerns are higher among females and the drive for exercise is higher among males who used Instagram. It was found that body image concerns were higher among people who exercised regularly as well as among those who followed fitness related pages on Instagram as compared to those who did not. These results provide an insight into the sex differences between the variables and future directions can be aimed at conducting an in-depth analysis using body image, exercise motivation and social comparison.

Nicotinamide Prevents Retinal Vascular Dropout in a Rat Model of Ocular Hypertension and Supports Ocular Blood Supply in Glaucoma Patients.

To investigate whether nicotinamide (NAM) modulates retinal vasculature in glaucoma. This was a prospective controlled clinical trial investigating animal and human histopathology. Participants included normotensive and ocular hypertensive rats, postmortem human ocular tissue, glaucoma patients (n = 90), and healthy controls (n = 30). The study utilized histopathology, computer-assisted retinal vasculature analysis, optical coherence tomography angiography (OCTA), and NAM treatment. The main outcome measures included retinal vascular parameters in rats as assessed by AngioTool; retinal vasculature integrity in rats and humans as assessed by histopathology, antibody-staining, and ImageJ-based measurements; and retinal perfusion density (PD) and flux index in humans as assessed by OCTA. A number of vessel parameters were altered in ocular hypertension/glaucoma compared to healthy controls. NAM treatment improved the retinal vasculature in ocular hypertensive rats, with an increase in mean vessel area, percentage area covered by vessels, total vessel length, total junctions, and junction density as assessed by AngioTool (all P < 0.05); vessel wall integrity as assessed by VE-cadherin antibody staining was also improved (P < 0.01). In humans, as assessed by OCTA, increases in PD in the optic nerve head and macula complete image (0.7%, P = 0.04 and 1.0%, P = 0.002, respectively) in healthy controls, and an increase in the temporal quadrant of the macula (0.7%, P = 0.02) in glaucoma patients was seen after NAM treatment. NAM can prevent retinal vascular damage in an animal model of glaucoma. After NAM treatment, glaucoma patients and healthy controls demonstrated a small increase in retinal vessel parameters as assessed by OCTA.

Deep learning-assisted IoMT framework for cerebral microbleed detection

The Internet of Things (IoT), big data, and artificial intelligence (AI) are all key technologies that influence the formation and implementation of digital medical services. Building Internet of Medical Things (IoMT) systems that combine advanced sensors with AI-powered insights is critical for intelligent medical systems. This paper presents an IoMT framework for brain magnetic resonance imaging (MRI) analysis to lessen the unavoidable diagnosis and therapy faults that occur in human clinical settings for the accurate detection of cerebral microbleeds (CMBs). The problems in accurate CMB detection include that CMBs are tiny dots 5–10 mm in diameter; they are similar to healthy tissues and are exceedingly difficult to identify, necessitating specialist guidance in remote and underdeveloped medical centers. Secondly, in the existing studies, computer-aided diagnostic (CAD) systems are designed for accurate CMB detection, however, their proposed approaches consist of two stages. Potential candidate CMBs from the complete MRI image are selected in the first stage and then passed to the phase of false-positive reduction. These pre-and post-processing steps make it difficult to build a completely automated CAD system for CMB that can produce results without human intervention. Hence, as a key goal of this work, an end-to-end enhanced UNet-based model for effective CMB detection and segmentation for IoMT devices is proposed. The proposed system requires no pre-processing or post-processing steps for CMB segmentation, and no existing research localizes each CMB pixel from the complete MRI image input. The findings indicate that the suggested method outperforms in detecting CMBs in the presence of contrast variations and similarities with other normal tissues and yields a good dice score of 0.70, an accuracy of 99 %, as well as a false-positive rate of 0.002 %.© 2017 Elsevier Inc. All rights reserved.

Improved generative adversarial network with deep metric learning for missing data imputation

Incomplete data are ubiquitous in real-world computer vision tasks. Imputing missing data is crucial for modeling machine learning algorithms. Although existing methods, such as MisGAN, have achieved considerable success, two problems remain: (1) generating a complete image from random noise weakens the model’s imputation capability; (2) owing to the influence of random noise, the training of the generator is unstable. Therefore, this study proposes a novel approach that uses deep metric learning to enable MisGAN to perform multi-tasking missing data imputation. First, an image feature extraction network is applied to extract the semantic representation of the images. Then, deep metric learning is performed to learn good features embedding by maximizing inter-class variation and minimizing intra-class variation. Such an embedding replaces the random noise and is fed into MisGAN to obtain an improved generated complete image. Several experiments are conducted on datasets, demonstrating that the proposed method can significantly outperform baseline methods.

An efficient framework for optic disk segmentation and classification of Glaucoma on fundus images

Accurately segmenting the Optic Disk is a crucial step in classifying Glaucoma using Fundus images. Machine learning and artificial intelligence techniques are widely used in Glaucoma detection, and the main indicators observed in Fundus images are the presence of Papillary Atrophy, Cup to Disc Ratio values, diminishing Neural Retinal Rim (NRR), the Inferior Superior Nasal Temporal (ISNT) rule, and Cup Diameter. In this research, we investigated various segmentation and classification techniques that can be applied to Optic disk segmentation and classification of normal and glaucomatous eyes. The proposed method will be beneficial to clinicians and healthcare workers in facilities with limited resources. In this paper, histogram processing is used to determine the type of image, and based on this information; we decide whether the image requires segmentation. Some datasets in the standard dataset contain complete retinal images while others include segmented optic disks. The segmented images are directly given as input for classification using the proposed Convolutional Neural Network (CNN). For complete retinal images, segmentation is performed using the Simple Linear Iterative clustering (SLIC) and normalized graph cut algorithm. The proposed framework's performance is compared with that of pretrained neural networks, including VGG19, InceptionV3, and ResNet50V2, using major metrics. We trained and tested these architectures with 3115 images from six standard datasets. Our proposed framework outperforms all with an accuracy of 96.33 %.

Research on Image Super-Resolution Reconstruction Technology Based on Unsupervised Learning

Affected by the movement of drones, missiles, and other aircraft platforms and the limitation of the accuracy of image sensors, the obtained images have low-resolution and serious loss of image details. Aiming at these problems, this paper studies the image super-resolution reconstruction technology. Firstly, a natural image degradation model based on a generative adversarial network is designed to learn the degradation relationship between image blocks within the image; then, an unsupervised learning residual network is designed based on the idea of image self-similarity to complete image super-resolution reconstruction. The experimental results show that the unsupervised super-resolution reconstruction algorithm is equivalent to the mainstream supervised learning algorithm under ideal conditions. Compared to mainstream algorithms, this algorithm has significantly improved its various indicators in real-world environments under nonideal conditions.

АНТРОПОЛОГИЯ КИНО: КОНСТРУИРОВАНИЕ НОВОГО ЧЕЛОВЕКА НА ОСНОВЕ МОНТАЖНОГО ПРИНЦИПА МЫШЛЕНИЯ

The article discusses the editing principle in cinematography, thanks to which the artistic thinking of cinematographers is able to generate new images. In discussions about the possibility of constructing a new person by means of cinematography (based on the editing principle), philosophical anthropology actualizes traditional problematic issues about the relationship between the spiritual and the bodily in a constructed person, about his integrity. In this regard, the problem of the correlation of the author’s freedom of artistic experiment and ethical norms in the process of designing a complete human image requires a new understanding. It is concluded that the philosophical and anthropological interpretation of the specific features of the assembly principle of thinking in the construction of a new person with new qualities is relevant and in demand in the context of the loss of human integrity.

Neuroimaging and Biosample Collection in the Toronto Adolescent and Youth Cohort Study: Rationale, Methods, and Early Data

BackgroundThe Toronto Adolescent and Youth (TAY) Cohort Study will characterize the neurobiological trajectories of psychosis spectrum symptoms, functioning, and suicidality (i.e., suicidal thoughts and behaviors) in youth seeking mental health care. Here, we present the neuroimaging and biosample component of the protocol. We also present feasibility and quality control metrics for the baseline sample collected thus far. MethodsThe current study includes youths (ages 11–24 years) who were referred to child and youth mental health services within a large tertiary care center in Toronto, Ontario, Canada, with target recruitment of 1500 participants. Participants were offered the opportunity to provide any or all of the following: 1) 1-hour magnetic resonance imaging (MRI) scan (electroencephalography if ineligible for or declined MRI), 2) blood sample for genomic and proteomic data (or saliva if blood collection was declined or not feasible) and urine sample, and 3) heart rate recording to assess respiratory sinus arrhythmia. ResultsOf the first 417 participants who consented to participate between May 4, 2021, and February 2, 2023, 412 agreed to participate in the imaging and biosample protocol. Of these, 334 completed imaging, 341 provided a biosample, 338 completed respiratory sinus arrhythmia, and 316 completed all 3. Following quality control, data usability was high (MRI: T1-weighted 99%, diffusion-weighted imaging 99%, arterial spin labeling 90%, resting-state functional MRI 95%, task functional MRI 90%; electroencephalography: 83%; respiratory sinus arrhythmia: 99%). ConclusionsThe high consent rates, good completion rates, and high data usability reported here demonstrate the feasibility of collecting and using brain imaging and biosamples in a large clinical cohort of youths seeking mental health care.

A New Image Segmentation Method Based on the YOLO5 and Fully Connected CRF

When manually polishing blades, skilled workers can quickly machine a blade by observing the characteristics of the polishing sparks. To help workers better recognize spark images, we used an industrial charge-coupled device (CCD) camera to capture the spark images. Firstly, the spark image region detected by yolo5, then segment from the background. Secondly, the target region was further segmented and refined in a fully connected conditional random field (CRF), from which the complete spark image obtained. Experimental results showed that this method could quickly and accurately segment whole spark image. The test results showed that this method was better than other image segmentation algorithms. Our method could better segment irregular image, improve recognition and segmentation efficiency of spark image, achieve automatic image segmentation, and replace human observation.

Direct comparison of the safety and efficacy of the ESC 0/1h-algorithm and the High-STEACS pathway for early rule-out of myocardial infarction

Abstract Background The optimal approach for early rule out of myocardial infarction (MI) is still uncertain. The European Society of Cardiology (ESC) 0/1h-algorithm and the High-Sensitivity Troponin in the Evaluation of Patients with Acute Coronary Syndrome (High-STEACS) pathway, have both been validated individually in large studies. However, as a direct comparison of both strategies has not been performed until now, it is largely unknown whether one is preferable to the other. Purpose To directly compare the safety and efficacy of the ESC 0/1h-algorithm with the High-STEACS pathway. Methods Patients presenting with acute chest discomfort to the emergency department were prospectively enrolled into an international multicenter diagnostic study. Final diagnoses were centrally adjudicated by two independent cardiologists applying the 4th universal definition of MI, based on complete cardiac work-up, cardiac imaging and serial hs-cTnI. Hs-cTnI (Architect) concentrations were measured at presentation, after 1h and 3h. The primary diagnostic endpoint was safety, assessed as the sensitivity and negative predictive value of ruling out index NSTEMI. The primary diagnostic endpoint was evaluated in prespecified subgroups stratified by sex, age, coronary artery disease, time of chest pain onset and glomerular filtration rate. Co-primary prognostic endpoint was 30- and 730- days all-cause mortality. Findings were externally validated in a large single center study. Results Among 5377 eligible patients, 1088 (20.2%) were finally adjudicated with the diagnosis of NSTEMI. Application of the ESC 0/1h-hs-cTnI-algorithm provided higher sensitivity (99.7 % [95% CI, 99.4-100]) compared to the High-STEACS pathway (99.0% [95% CI, 98.4 – 99.6], difference in sensitivity 0.7 [1.3-0.2], P-value for difference &amp;lt; 0.01), but left 846 patients (15.7%) without final triage towards rule-out or rule-in. Three patients with NSTEMI were missed by the ESC pathway compared to 11 patients missed by the High-STEACS algorithm (Figure). High safety and accuracy for the ESC pathway were confirmed in all subgroups and in the external validation cohort, whereas performance parameters for the High-STEACS pathway dropped considerably in the subgroup- and external validation analysis. Both strategies allowed reliable prediction of short and long-term mortality risk (log-rank test &amp;lt;0.001). Conclusion While the High-STEACS pathway identifies more patients at low-risk, the ESC 0/1h-algorithm provides higher sensitivity and NPV for rule-out of index NSTEMI.Performance of the ESC 0/1-h algorithmPerformance of the High-STEACS pathway

Abstract 15036: Identification of Fabry Disease Cardiac Phenotype in Hypertrophic Cardiomyopathy With Cardiac Magnetic Resonance Imaging Technology

Introduction: Cardiac magnetic resonance(CMR), especially T1 mapping, has an important role in the diagnosis of Fabry disease, but the differential value in myocardial diseases with hypertrophic phenotype needs further study. More importantly, there has been no systematic study of the difference between different CMR techniques indistinguishing Fabry cardiomyopathy from hypertrophic cardiomyopathy (HCM). Hypothesis: Myomapping technology can be a parameter in distinguishing Fabry cardiomyopathy from HCM. Methods: We continuously included patients with clinically diagnosed hypertrophic cardiomyopathy, completed systematic magnetic resonance imaging, and used genetic testing as the diagnostic criteria. We reported the prevalence rate in the population of HCM, and deeply conducted comparative studies on the differences in magnetic resonance characteristics between Fabry disease and HCM. We also analyzed the diagnostic value of magnetic resonance imaging in distinguishing Fabry cardiomyopathy from HCM. Results: We continuously included patients diagnosed with hypertrophic cardiomyopathy at West China Hospital from 2012 to 2022 and completed systematic magnetic resonance imaging examinations. We reported the prevalence of Fabry cardiomyopathy in the population using genetic testing as the diagnostic criteria, and deeply conducted comparative studies on the differences in CMR features between Fabry disease and HCM. We also analyzed the diagnostic value of CMR in distinguishing Fabry cardiomyopathy from HCM. Conclusions: We found a prevalence of 13 (2.6%) cases of Fabry disease in the hypertrophic cardiomyopathy cohort (494 cases). Our further analysis found that there were no significant differences in the degree of hypertrophy, overall cardiac function and size of Fabry patients compared with patients with HCM, as well as strain analysis. NativeT1 was significantly reduced in Fabry disease, and segmental analysis found that the basal and middle ventricular septum and anterior wall were the most obvious. ECV differed significantly between the two groups. There was no difference in T2 between the two groups. ROC analysis showed that T1 had the most discriminating value

Clinical and radiologic differences between early onset, late onset, and very late onset adult multiple sclerosis

BackgroundPrevious studies suggested possible differences in clinical and radiologic characteristics between early-onset multiple sclerosis (EOMS) and late-onset MS (LOMS). However, differences between LOMS and very late onset MS (VLOMS) are largely unknown. MethodsWe performed a retrospective review of medical records of patients diagnosed with MS between 8/1/2017 and 3/1/2022 at the comprehensive MS center of the Froedtert and Medical College of Wisconsin. We included adult patients with MS diagnosis who were 60 years or older - VLOMS, 50–59 years old at diagnosis – LOMS, or were 18–30 years old at diagnosis – EOMS and had complete imaging and clinical records. Clinical presentation and location of demyelinating lesions at the onset of diagnosis were extracted and compared using the chi-square test, p<0.05. ResultsA total of 246 newly diagnosed patients were identified. Of which 54 were EOMS, 29 were LOMS, and 35 were VLOMS. The sex ratio was not different between groups. EOMS had a higher percentage of patients who self-identified as black, while LOMS had a higher percentage of patients who self-identified as white. LOMS and VLOMS showed significant differences in the presence of tremors and lesion distribution at the onset. Older onset patients were more likely to present with motor symptoms, sphincter dysfunction, fatigue, and tremor. EOMS was more likely to present with cerebellum and occipital lobe lesions, and lesions were more likely to show contrast enhancement on MRI at diagnosis. ConclusionOur findings revealed novel clinical and imaging characteristics differences between VLOMS and LOMS. The current classification of LOMS may benefit from revision to better align with chronological age classification for old age >60 instead of the current standard in the literature of >50 years.

Abstract 13286: Co-Morbidities Clustering According to Imaging Diagnostic Criteria in Cardiac Sarcoidosis

Background: Cardiac sarcoidosis (CS) diagnostic guidelines updated in 2006 and in 2017 now include imaging findings of left ventricular (LV) dysfunction (LV EF &lt; 50%) on cardiac MRI and echocardiography as major diagnostic criterion. Limited data is available with regards to co-morbidities clustering in sarcoidosis and CS according to the 2017 updated guideline diagnostic criteria. Methods: A case control, single tertiary medical center study included 558 sarcoidosis patients with documented extracardiac sarcoidosis and completed electrocardiogram and/or cardiac MRI imaging. CS 2006 and 2017 diagnostic criteria and co-morbidity data were extracted from electronic charts and were available for comparison in 540 patients. Results: The total study population was composed of 52.7% (281/540) females, aged 58.9+/-12.6 years old, with diabetes mellitus (DM) present in 29.6% (160/540), HTN in 51.7% (279/540), CAD in 13.3% (72/540), moderate to severe aortic, mitral, or tricuspid valvular disease in 14.8% (80/540), ESRD in 2.4% (13/540), and COPD or asthma in 21.9% (118/540). There was a significant clustering of co-morbidities according to the imaging CS diagnostic criteria. Patients meeting the 2017 CS imaging criteria were found to be older (61.4+/-11.3 vs 58.8+/-12.8 years old in patients not meeting criteria, p=0.085), predominantly male (69.2%, (54/78) vs. 44.4%, (205/462), p&lt;0.001), and demonstrated significantly increased prevalence of DM (39.7%, (31/78) vs. 27.9%, (129/462), p=0.034), and valvular disease (33.3%, (26/78) vs. 11.7%, (54/462), respectively, p&lt;0.001). However, there were no statistically significant difference in the prevalence of HTN (60.3%, (47/78) vs. 50.2%, (232/462), p=0.101), ESRD (3.9%, (3/76) vs. 2.2%, (10/456), p=0.361), and COPD or asthma (19.2%, (15/78) vs. 22.3%, (103/462), p=0.544) in patients meeting 2017 CS criteria compared to those that did not. Conclusion: CS patients meeting 2017 imaging diagnostic criteria were found to have statistically significant clustering of the following co-morbidities: male gender, DM, and valvular disease. These findings underscore importance of comprehensive care in CS patients and warrant caution in assigning LV abnormalities observed on imaging solely to CS etiology.

Alternativeness in the Architectural Design of Bridges and Overpasses

Modern extensive foreign and domestic experience in design and construction of multifunctional overpasses and bridges indicates that they have become an integral part of urbanised systems. They are endowed with the functions of important urban objects, and with their appearance they have won the right to be considered iconic structures. The subject of the article refers to architectural design of bridges and overpasses. The authors set themselves the task of considering the aspects of combining the main functional characteristics with the aesthetic properties of the object, designating visual components of the project and the specifics of alternativeness of design.The objective of the work is to consider alternativeness in architectural design of bridges and overpasses. A complex method, combining an analysis of the historical and modern experience of architectural theory and design practices, was based on technical literary materials. The study resulted in formulating indicators and leading principles of alternative design.The use of alternatives in the process of designing urban bridges and overpasses allows not only to consider functionality and economic feasibility of a particular project and construction of an object, but also its architectural and artistic orientation, aesthetic component, as well as the designer’s worldview and current trends in architecture. In the process of alternative design of bridges and overpasses, it is required to reproduce the overall complete image of the future structure, consider all kinds of details in structures and shape, and various criteria for layout and location in the environment.