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Background: Acute ischemic stroke (AIS) is a complication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We aimed to explore neurovascular imaging patterns in patients with SARS-CoV-2-related AIS., Methods: We retrospectively analyzed clinical and radiological data of patients hospitalized with AIS and a positive PCR test for SARS-CoV-2 prior to AIS onset. The control group comprised of AIS patients from a pre-COVID-19 pandemic period matched for gender and age., Results: Thirty-five SARS-CoV-2-related stroke patients, and 35 controls were included. Fifty-seven percent of SARS-CoV-2 patients had either mild or asymptomatic disease. A distinctive imaging pattern of floating arterial mural thrombus was detected in 5 patients of the SARS-CoV-2 group. In 4 patients thrombus was attached to a stenotic atherosclerotic plaque in the proximal internal carotid artery. In the 5th patient a cardiac CTA showed multiple floating thrombi in the descending aorta. In the control group, floating thrombus was only detected in one patient. Treatment with dual antiplatelet therapy was associated with thrombus dissolution and good clinical outcome. Patients with floating thrombi had a longer time from SARS-CoV-2 diagnosis to stroke onset (mean 7.4 versus 3.4 days)., Conclusions: Floating arterial mural thrombi attached to atherosclerotic plaques are unique characteristic source of AIS in SARS-CoV-2 patients. They may lead to ischemic stroke in patients with mild or asymptomatic infection up to 1-2 weeks from SARS-CoV-2 diagnosis. Patients with embolic AIS and SARS-CoV-2 diagnosis should perform high resolution cranio-cervical vascular imaging to evaluate floating thrombi as a potential embolic source., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Pardo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The use of prone position (PP) has been widespread during the COVID-19 pandemic. Whereas it has demonstrated benefits, including improved oxygenation and lung aeration, the factors influencing the response in terms of gas exchange to PP remain unclear. In particular, the association between baseline quantitative computed tomography (CT) scan results and gas exchange response to PP in invasively ventilated subjects with COVID-19 ARDS is unknown. The present study aimed to compare baseline quantitative CT results between subjects responding to PP in terms of oxygenation or CO 2 clearance and those who did not., Methods: This was a single-center, retrospective observational study including critically ill, invasively ventilated subjects with COVID-19-related ARDS admitted to the ICUs of Niguarda Hospital between March 2020-November 2021. Blood gas samples were collected before and after PP. Subjects in whom the P aO 2 /F IO 2 increase was ≥ 20 mm Hg after PP were defined as oxygen responders. CO 2 responders were defined when the ventilatory ratio (VR) decreased during PP. Automated quantitative CT analyses were performed to obtain tissue mass and density of the lungs., Results: One hundred twenty-five subjects were enrolled, of which 116 (93%) were O 2 responders and 51 (41%) CO 2 responders. No difference in quantitative CT characteristics and oxygen were observed between responders and non-responders (tissue mass 1,532 ± 396 g vs 1,654 ± 304 g, P = .28; density -544 ± 109 HU vs -562 ± 58 HU P = .42). Similar findings were observed when dividing the population according to CO 2 response (tissue mass 1,551 ± 412 g vs 1,534 ± 377 g, P = .89; density -545 ± 123 HU vs -546 ± 94 HU, P = .99)., Conclusions: Most subjects with COVID-19-related ARDS improved their oxygenation at the first pronation cycle. The study suggests that baseline quantitative CT scan data were not associated with the response to PP in oxygenation or CO 2 in mechanically ventilated subjects with COVID-19-related ARDS., Competing Interests: The authors have disclosed no conflicts of interest., (Copyright © 2024 by Daedalus Enterprises.)

In light of the ongoing battle against COVID-19, while the pandemic may eventually subside, sporadic cases may still emerge, underscoring the need for accurate detection from radiological images. However, the limited explainability of current deep learning models restricts clinician acceptance. To address this issue, our research integrates multiple CNN models with explainable AI techniques, ensuring model interpretability before ensemble construction. Our approach enhances both accuracy and interpretability by evaluating advanced CNN models on the largest publicly available X-ray dataset, COVIDx CXR-3, which includes 29,986 images, and the CT scan dataset for SARS-CoV-2 from Kaggle, which includes a total of 2,482 images. We also employed additional public datasets for cross-dataset evaluation, ensuring a thorough assessment of model performance across various imaging conditions. By leveraging methods including LIME, SHAP, Grad-CAM, and Grad-CAM++, we provide transparent insights into model decisions. Our ensemble model, which includes DenseNet169, ResNet50, and VGG16, demonstrates strong performance. For the X-ray image dataset, sensitivity, specificity, accuracy, F1-score, and AUC are recorded at 99.00%, 99.00%, 99.00%, 0.99, and 0.99, respectively. For the CT image dataset, these metrics are 96.18%, 96.18%, 96.18%, 0.9618, and 0.96, respectively. Our methodology bridges the gap between precision and interpretability in clinical settings by combining model diversity with explainability, promising enhanced disease diagnosis and greater clinician acceptance., (© 2024. The Author(s).)

Background: The increasing use of CT imaging in emergency departments, despite efforts of reducing low-value imaging, is not fully understood, especially during and after the COVID-19 pandemic. The aim of this study was to investigate the impact of COVID-19 pandemic related measures on trends and volume in CT examinations requested in the emergency department., Methods: CT examinations of the head, chest, and/or abdomen-pelvis (n = 161,008), and chest radiographs (n = 113,240) performed at our tertiary care hospital between 01/2014 and 12/2023 were retrospectively analyzed. CT examinations (head, chest, abdomen, dual-region and polytrauma) and chest radiographs requested by the emergency department during (03/2020-03/2022) and after the COVID-19 pandemic (04/2022-12/2023) were compared to a pre-pandemic control period (02/2018-02/2020). Analyses included CT examinations per emergency department visit, and prediction models based on pre-pandemic trends and inpatient data. A regular expressions text search algorithm determined the most common clinical questions., Results: The usage of dual-region and chest CT examinations were higher during (+ 116,4% and + 115.8%, respectively; p < .001) and after the COVID-19 pandemic (+ 88,4% and + 70.7%, respectively; p < .001), compared to the control period. Chest radiograph usage decreased (-54.1% and - 36.4%, respectively; p < .001). The post-pandemic overall CT examination rate per emergency department visit increased by 4.7%. The prediction model underestimated (p < .001) the growth (dual-region CT: 22.3%, chest CT: 26.7%, chest radiographs: -30.4%), and the rise (p < .001) was higher compared to inpatient data (dual-region CT: 54.8%, chest CT: 52.0%, CR: -32.3%). Post-pandemic, the number of clinical questions to rule out "pulmonary infiltrates", "abdominal pain" and "infection focus" increased up to 235.7% compared to the control period., Conclusions: Following the COVID-19 pandemic, chest CT and dual-region CT usage in the emergency department experienced a disproportionate and sustained surge compared to pre-pandemic growth., (© 2024. The Author(s).)

Body measurements are primarily made with a tape measure. In measurements taken with a tape measure, the inability to take measurements from the same part of the body each time, incorrect positioning of the tape measure, the occurrence of incorrect measurements, and the need for a person to take the measurements are significant problems in the traditional measurement method. Due to the social distancing rule that must be followed during the Covid-19 pandemic, the close contact between the person to be measured and the person taking the measurement became the starting point of this study. This study focuses on the detecting body shape changes using image processing techniques with 2D imaging. The novelty of the work is that non-contact body measurements are taken more accurately and reliably using the cosine theorem. Regular monitoring of obese patients is important in combating obesity, which is also the source of many health problems. In the monitoring of obese patients, it is necessary to determine the rate of slimming in areas where fat accumulation is intense. The error margin between the real measurements of human models and the calculated measurements was calculated as an average of ± 5.16% for waistline and an average of ± 4.58% for hip size. The cosine theorem was used instead of the ellipse formula used in the literature, and it was observed that the cosine theorem obtained results closer to reality. It is also thought that the developed system will be beneficial not only for extracting body measurements but also for extracting body measurements contactless in the textile sector. The study demonstrates the feasibility of image processing for non-contact body anthropometry and shape tracking., (© 2024. The Author(s).)

Background: The pathophysiology of protracted symptoms after COVID-19 is unclear. This study aimed to determine if long-COVID is associated with differences in baseline characteristics, markers of white matter diffusivity in the brain, and lower scores on objective cognitive testing., Methods: Individuals who experienced COVID-19 symptoms for more than 60 days post-infection (long-COVID) (n = 56) were compared to individuals who recovered from COVID-19 within 60 days of infection (normal recovery) (n = 35). Information regarding physical and mental health, and COVID-19 illness was collected. The National Institute of Health Toolbox Cognition Battery was administered. Participants underwent magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI). Tract-based spatial statistics were used to perform a whole-brain voxel-wise analysis on standard DTI metrics (fractional anisotropy, axial diffusivity, mean diffusivity, radial diffusivity), controlling for age and sex. NIH Toolbox Age-Adjusted Fluid Cognition Scores were used to compare long-COVID and normal recovery groups, covarying for Age-Adjusted Crystallized Cognition Scores and years of education. False discovery rate correction was applied for multiple comparisons., Results: There were no significant differences in age, sex, or history of neurovascular risk factors between the groups. The long-COVID group had significantly (p < 0.05) lower mean diffusivity than the normal recovery group across multiple white matter regions, including the internal capsule, anterior and superior corona radiata, corpus callosum, superior fronto-occiptal fasciculus, and posterior thalamic radiation. However, the effect sizes of these differences were small (all β<|0.3|) and no significant differences were found for the other DTI metrics. Fluid cognition composite scores did not differ significantly between the long-COVID and normal recovery groups (p > 0.05)., Conclusions: Differences in diffusivity between long-COVID and normal recovery groups were found on only one DTI metric. This could represent subtle areas of pathology such as gliosis or edema, but the small effect sizes and non-specific nature of the diffusion indices make pathological inference difficult. Although long-COVID patients reported many neuropsychiatric symptoms, significant differences in objective cognitive performance were not found., Competing Interests: Declaration of competing interest WGH is a Consultant to Newron, AbbVie and Boehringer Ingelheim. Other authors have no conflicts of interest to declare., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Chlamydia psittaci pneumonia (CPP) exhibits similar characteristics as of COVID-19 with respect to clustering outbreaks and onset symptoms. This study is aimed at exploring the different clinical manifestations of both pneumonias to establish a simple nomogram to distinguish them. This multicenter, retrospective, case-control study compared two independent cohorts of patients with CPP or COVID-19. The risk factors of CPP were analyzed using multivariate logistic regression, which was used to establish the nomogram. Both patients with CPP and COVID-19 exhibited similar clinical symptoms. As compared to patients with COVID-19, a higher proportion of patients with CPP had nervous system symptoms. Patients with CPP had higher inflammatory indicators, creatine kinase, and lower lymphocyte and albumin. They also had lower proportions of ground-glass opacity and bilateral lung involvement than COVID-19 patients. Furthermore, patients with CPP had higher 30 day mortality as well as higher rates of severe pneumonia, septic shock, and ICU admission. Multivariate logistic regression showed that nervous system symptoms, lymphocytes, creatine kinase, bilateral lung lesions, and ground-glass opacity were risk factors for CPP. Incorporating these five factors, the nomogram achieved good concordance index of 0.989 in differentiating CPP from COVID-19, and had well-fitted calibration curves. Despite similar clinical characteristics, nervous system symptoms, lymphocyte, creatine kinase, lesions in bilateral lungs, and ground-glass opacity may help in differentiating the pneumonias. These were combined into a clinically useful nomogram for rapid and early identification of CPP to avoid misdiagnosis and help in the decision-making process., (© 2024. The Author(s).)

Accurate segmentation of COVID-19 lesions from medical images is essential for achieving precise diagnosis and developing effective treatment strategies. Unfortunately, this task presents significant challenges, owing to the complex and diverse characteristics of opaque areas, subtle differences between infected and healthy tissue, and the presence of noise in CT images. To address these difficulties, this paper designs a new deep-learning architecture (named MD-Net) based on multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation. In our framework, the U-shaped structure serves as the cornerstone to facilitate complex hierarchical representations essential for accurate segmentation. Then, by introducing the multi-scale input layers (MIL), the network can effectively analyze both fine-grained details and contextual information in the original image. Furthermore, we introduce an SE-Conv module in the encoder network, which can enhance the ability to identify relevant information while simultaneously suppressing the transmission of extraneous or non-lesion information. Additionally, we design a dense decoder aggregation (DDA) module to integrate feature distributions and important COVID-19 lesion information from adjacent encoder layers. Finally, we conducted a comprehensive quantitative analysis and comparison between two publicly available datasets, namely Vid-QU-EX and QaTa-COV19-v2, to assess the robustness and versatility of MD-Net in segmenting COVID-19 lesions. The experimental results show that the proposed MD-Net has superior performance compared to its competitors, and it exhibits higher scores on the Dice value, Matthews correlation coefficient (Mcc), and Jaccard index. In addition, we also conducted ablation studies on the Vid-QU-EX dataset to evaluate the contributions of each key component within the proposed architecture., (© 2024. The Author(s).)

The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions. This rise has led to the development of Explainable AI (XAI) system to improve the transparency and trust of complex DL methods. However, XAI systems face challenges in gaining acceptance within the healthcare sector, mainly due to technical hurdles in utilizing these systems in practice and the lack of human-centered evaluation/validation. In this study, we focus on visual XAI systems applied to DL-enabled diagnostic system in chest radiography. In particular, we conduct a user study to evaluate two prominent visual XAI techniques from the human perspective. To this end, we created two clinical scenarios for diagnosing pneumonia and COVID-19 using DL techniques applied to chest X-ray and CT scans. The achieved accuracy rates were 90% for pneumonia and 98% for COVID-19. Subsequently, we employed two well-known XAI methods, Grad-CAM (Gradient-weighted Class Activation Mapping) and LIME (Local Interpretable Model-agnostic Explanations), to generate visual explanations elucidating the AI decision-making process. The visual explainability results were shared through a user study, undergoing evaluation by medical professionals in terms of clinical relevance, coherency, and user trust. In general, participants expressed a positive perception of the use of XAI systems in chest radiography. However, there was a noticeable lack of awareness regarding their value and practical aspects. Regarding preferences, Grad-CAM showed superior performance over LIME in terms of coherency and trust, although concerns were raised about its clinical usability. Our findings highlight key user-driven explainability requirements, emphasizing the importance of multi-modal explainability and the necessity to increase awareness of XAI systems among medical practitioners. Inclusive design was also identified as a crucial need to ensure better alignment of these systems with user needs., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 E. Ihongbe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

To improve the prognosis of patients suffering from pulmonary diseases, such as lung cancer, early diagnosis and treatment are crucial. The analysis of CT images is invaluable for diagnosis, whereas high quality segmentation of the airway tree are required for intervention planning and live guidance during bronchoscopy. Recently, the Multi-domain Airway Tree Modeling (ATM'22) challenge released a large dataset, both enabling training of deep-learning based models and bringing substantial improvement of the state-of-the-art for the airway segmentation task. The ATM'22 dataset includes a large group of COVID'19 patients and a range of other lung diseases, however, relatively few patients with severe pathologies affecting the airway tree anatomy was found. In this study, we introduce a new public benchmark dataset (AeroPath), consisting of 27 CT images from patients with pathologies ranging from emphysema to large tumors, with corresponding trachea and bronchi annotations. Second, we present a multiscale fusion design for automatic airway segmentation. Models were trained on the ATM'22 dataset, tested on the AeroPath dataset, and further evaluated against competitive open-source methods. The same performance metrics as used in the ATM'22 challenge were used to benchmark the different considered approaches. Lastly, an open web application is developed, to easily test the proposed model on new data. The results demonstrated that our proposed architecture predicted topologically correct segmentations for all the patients included in the AeroPath dataset. The proposed method is robust and able to handle various anomalies, down to at least the fifth airway generation. In addition, the AeroPath dataset, featuring patients with challenging pathologies, will contribute to development of new state-of-the-art methods. The AeroPath dataset and the web application are made openly available., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Støverud et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) impacts multiple anatomical sites. Whether this is due to the virus itself or is a secondary effect caused by the influx and activation of immune cells is not known. Positron emission tomography (PET) with immunoglobulins can provide insights into which sites and cells are activated in a living animal. Our aim is to use two nanobodies as tools to monitor (1) the distribution of antigen presenting cells (APC) by virtue of their Mafa-DR expression profile, (2) virus-infected cells and viral particles using a nanobody against the SARS-CoV-2 spike protein. Two [ 89 Zr]-labeled nanobodies that target the SARS-CoV-2 spike protein and major histocompatability complex (MHC) class II antigens (Mafa-DR), respectively, are used to monitor their distribution during an experimental SARS-CoV-2 infection in a nonhuman primate model. Scans are obtained before infection and on Day 3 and 10 post infection (pi) in two macaques each. The [ 89 Zr]anti-SARS-CoV-2 spike nanobody localized to SARS-CoV-2-associated lung lesions and the nasal mucosa, while the [ 89 Zr]anti-human leukocyte antigen (HLA)-DR nanobody was predominantly found in non-affected lung tissue after infection. We also detected, pi, upregulation of the Mafa-DR signal, indicative of recruitment of professional APCs, in the superior sagittal sinus. [ 89 Zr]-labeled nanobodies show recruitment of macrophages/monocytes in non-lesional lung tissue in cynomolgus macaques after experimental infection with SARS-CoV-2, as well as accumulation of the spike protein in both lung lesions and the nasal mucosa during infection. These results show the possibility of in vivo monitoring the quality and quantity of immune responses during the initial stages of an infection., (© 2024 The Author(s). Journal of Medical Virology published by Wiley Periodicals LLC.)

Purpose: To assess the diagnostic accuracy of ChatGPT-4V in interpreting a set of four chest CT slices for each case of COVID-19, non-small cell lung cancer (NSCLC), and control cases, thereby evaluating its potential as an AI tool in radiological diagnostics., Materials and Methods: In this retrospective study, 60 CT scans from The Cancer Imaging Archive, covering COVID-19, NSCLC, and control cases were analyzed using ChatGPT-4V. A radiologist selected four CT slices from each scan for evaluation. ChatGPT-4V's interpretations were compared against the gold standard diagnoses and assessed by two radiologists. Statistical analyses focused on accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), along with an examination of the impact of pathology location and lobe involvement., Results: ChatGPT-4V showed an overall diagnostic accuracy of 56.76%. For NSCLC, sensitivity was 27.27% and specificity was 60.47%. In COVID-19 detection, sensitivity was 13.64% and specificity of 64.29%. For control cases, the sensitivity was 31.82%, with a specificity of 95.24%. The highest sensitivity (83.33%) was observed in cases involving all lung lobes. The chi-squared statistical analysis indicated significant differences in Sensitivity across categories and in relation to the location and lobar involvement of pathologies., Conclusion: ChatGPT-4V demonstrated variable diagnostic performance in chest CT interpretation, with notable proficiency in specific scenarios. This underscores the challenges of cross-modal AI models like ChatGPT-4V in radiology, pointing toward significant areas for improvement to ensure dependability. The study emphasizes the importance of enhancing these models for broader, more reliable medical use., (© 2024. The Author(s).)

Objectives: To investigate potential presence and resolution of longer-term pulmonary diffusion limitation and microvascular perfusion impairment in COVID-19 convalescents., Materials and Methods: This prospective, longitudinal study was carried out between May 2020 and April 2023. COVID-19 convalescents repeatedly and age/sex-matched healthy controls once underwent MRI including hyperpolarized 129 Xe MRI. Blood samples were obtained in COVID-19 convalescents for immunophenotyping. Ratios of 129 Xe in red blood cells (RBC), tissue/plasma (TP), and gas phase (GP) as well as lung surface-volume ratio were quantified and correlations with CD4 + /CD8 + T cell frequencies were assessed using Pearson's correlation coefficient. Signed-rank tests were used for longitudinal and U tests for group comparisons., Results: Thirty-five participants were recruited. Twenty-three COVID-19 convalescents (age 52.1 ± 19.4 years, 13 men) underwent baseline MRI 12.6 ± 4.2 weeks after symptom onset. Fourteen COVID-19 convalescents underwent follow-up MRI and 12 were included for longitudinal comparison (baseline MRI at 11.5 ± 2.7 weeks and follow-up 38.0 ± 5.5 weeks). Twelve matched controls were included for comparison. In COVID-19 convalescents, RBC-TP was increased at follow-up (p = 0.04). Baseline RBC-TP was lower in patients treated on intensive care unit (p = 0.03) and in patients with severe/critical disease (p = 0.006). RBC-TP correlated with CD4 + /CD8 + T cell frequencies (R = 0.61/ - 0.60) at baseline. RBC-TP was not significantly different compared to matched controls at follow-up (p = 0.25)., Conclusion: Impaired microvascular pulmonary perfusion and alveolar membrane function persisted 12 weeks after symptom onset and resolved within 38 weeks after COVID-19 symptom onset., Clinical Relevance Statement: 129 Xe MRI shows improvement of microvascular pulmonary perfusion and alveolar membrane function between 11.5 ± 2.7 weeks and 38.0 ± 5.5 weeks after symptom onset in patients after COVID-19, returning to normal in subjects without significant prior disease., Key Points: • The study aims to investigate long-term effects of COVID-19 on lung function, in particular gas uptake efficiency, and on the cardiovascular system. • In COVID-19 convalescents, the ratio of 129 Xe in red blood cells/tissue plasma increased longitudinally (p = 0.04), but was not different from matched controls at follow-up (p = 0.25). • Microvascular pulmonary perfusion and alveolar membrane function are impaired 11.5 weeks after symptom onset in patients after COVID-19, returning to normal in subjects without significant prior disease at 38.0 weeks., (© 2024. The Author(s).)

Background: Body mass index (BMI) is criticized for being unjust and biased in relatively healthy racial and ethnic groups. Therefore, the current analysis examines if BMI predicts body composition, specifically adiposity, in a racially and ethnically diverse acutely ill patient population., Methods: Patients admitted with SARS-CoV-2 having an evaluable diagnostic chest, abdomen, and/or pelvic computed tomography (CT) study (within 5 days of admission) were included in this retrospective cohort. Cross-sectional areas (centimeters squared) of the subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intramuscular adipose tissue (IMAT) were quantified. Total adipose tissue (TAT) was calculated as sum of these areas. Admission height and weight were applied to calculate BMI, and self-reported race and ethnicity were used for classification. General linear regression models were conducted to estimate correlations and assess differences between groups., Results: On average, patients (n = 134) were aged 58.2 (SD = 19.1) years, 60% male, and racially and ethnically diverse (33% non-Hispanic White [NHW], 33% non-Hispanic Black [NHB], 34% Hispanic). Correlations between BMI and SAT and BMI and TAT were strongest revealing estimates of 0.707 (0.585, 0.829) and 0.633 (0.534, 0.792), respectively. When examining the various adiposity compartments across race and ethnicity, correlations were similar and significant differences were not detected for TAT with SAT, VAT, or IMAT (all P ≥ 0.05)., Conclusions: These findings support the routine use of applying BMI as a proxy measure of total adiposity for acutely ill patients identifying as NHW, NHB, and Hispanic. Our results inform the validity and utility of this tool in clinical nutrition practice., (© 2024 The Author(s). Nutrition in Clinical Practice published by Wiley Periodicals LLC on behalf of American Society for Parenteral and Enteral Nutrition.)

Objective: Many patients with long COVID experience neurological and psychological symptoms. Signal abnormalities on MR images in the corpus callosum have been reported. Knowledge about the metabolic profile in the splenium of the corpus callosum (CCS) may contribute to a better understanding of the pathophysiology of long COVID., Materials and Methods: Eighty-one subjects underwent proton MR spectroscopy examination. The metabolic concentrations of total N-acetylaspartate (NAA), choline-containing compounds (Cho), total creatine (Cr), myo-inositol (mI), and NAA/Cho in the CCS were statistically compared in the group of patients containing 58 subjects with positive IgG COVID-19 antibodies or positive SARS-CoV-2 qPCR test at least two months before the MR and the group of healthy controls containing 23 subjects with negative IgG antibodies., Results: An age-dependent effect of SARS-CoV-2 on Cho concentrations in the CCS has been observed. Considering the subjective threshold of age = 40 years, older patients showed significantly increased Cho concentrations in the CCS than older healthy controls (p = 0.02). NAA, Cr, and mI were unchanged. All metabolite concentrations in the CCS of younger post-COVID-19 patients remained unaffected by SARS-CoV-2. Cho did not show any difference between symptomatic and asymptomatic patients (p = 0.91)., Discussion: Our results suggest that SARS-CoV-2 disproportionately increases Cho concentration in the CCS among older post-COVID-19 patients compared to younger ones. The observed changes in Cho may be related to the microstructural reorganization in the CCS also reported in diffusion measurements rather than increased membrane turnover. These changes do not seem to be related to neuropsychological problems of the post-COVID-19 patients. Further metabolic studies are recommended to confirm these observations., (© 2024. The Author(s).)

Background: During the COVID-19 pandemic individuals with all blood cancers were classified as clinically vulnerable and at high risk of complications and death. Our study sought to determine if individuals with specific blood cancers were at a heightened risk of longer term organ impairment, secondary to SARS-CoV-2 infection., Methods: We set up a prospective observational study, utilising quantitative multi-parametric MRI to determine organ health over time in patients with specific blood cancers who had recovered from COVID-19., Results: Multi-organ abnormality was more prevalent in blood cancer patients than in healthy controls (42 % vs 6 % p < 0.001) but comparable to the long COVID controls (42 % vs 33 %, p > 0.05). At 6 month follow up scans, organ abnormalities persisted in most individuals with blood cancer (71 % ≥1 organ and 52 % multi-organ)., Conclusion: A multi-organ MRI platform offers the capacity to accurately evaluate organ health dynamically in blood cancers and detect asymptomatic organ impairment. The application of multi-organ MRI could aid early detection and longitudinal monitoring of organ impairment, potentially guiding more personalised treatment strategies and improving clinical outcomes in many rare diseases., Competing Interests: Declaration of competing interest MP: Perspectum Ltd: Current Employment as Consultant, Current equity holder in private company. CD: Perspectum Ltd: Current Employment. HTB: Perspectum Ltd: Current Employment, Current equity holder in private company, Current holder of stock options in a privately-held company., (Copyright © 2024 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.)

Abstract: A 65-year-old woman with long-standing symptoms of COVID-19 participated in our clinical study on the role of 68 Ga-FAPI-04 PET/CT in pulmonary fibrosis. Neither sequela parenchymal changes nor 68 Ga-FAPI-04 uptake was observed in the lungs. However, focal 68 Ga-FAPI-04 uptake was localized in a smoothly circumscribed mass observed in the right frontal region. A meningioma diagnosis was made based on the radiological findings., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

During the COVID-19 pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected millions of people worldwide, with acute respiratory distress syndrome (ARDS) being the most common severe condition of pulmonary involvement. Despite its involvement in the lungs, SARS-CoV-2 causes multiple extrapulmonary manifestations, including manifestations in the musculoskeletal system. Several cases involving bone, joint, muscle, neurovascular and soft tissues were reported shortly after pandemic onset. Even after the acute infection has resolved, many patients experience persistent symptoms and a decrease in quality of life, a condition known as post-COVID syndrome or long COVID. COVID-19 vaccines have been widely available since December 2020, preventing millions of deaths during the pandemic. However, adverse reactions, including those involving the musculoskeletal system, have been reported in the literature. Therefore, the primary goal of this article is to review the main imaging findings of SARS-CoV-2 involvement in the musculoskeletal system, including acute, subacute, chronic and postvaccination manifestations., (© 2023. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Objective: This study seeks to evaluate how recent COVID-19 infection affects myocardial perfusion and functional parameters derived from gated myocardial perfusion imaging in patients undergoing evaluation for coronary artery disease. The goal is to enhance our understanding of COVID-19's influence on the cardiovascular system., Method: Conducted at Farshchian Heart Hospital from 2022 to 2023, this case-control study enrolled patients suspected of coronary artery disease, stratified into two groups: those with confirmed COVID-19 infection within the past 6 months (study group) and those without prior COVID-19 infection (control group). Employing a 2-day protocol, stress testing and gated SPECT MPI were performed. Statistical analysis included descriptive statistics, Chi-square test, Student's t test, and Mann-Whitney U test., Result: Among the 86 patients included, 43 were in each group. Significantly higher summed stress core and summed difference score values were observed in the study group compared to the control group (p < 0.05). In addition, the study group exhibited significantly altered global left ventricular ejection fraction, end-diastolic volume, and end-systolic volume (p < 0.05). Non-perfusion findings, including transient ischemic dilation and transient right ventricular visualization, were more prevalent in the study group., Conclusion: Recent COVID-19 infection is associated with impaired myocardial perfusion and altered functional parameters as detected by MPI. These findings underscore the intricate interplay between COVID-19 and cardiovascular health, emphasizing the importance of comprehensive evaluation and management strategies to address cardiac complications in affected individuals. Further research is warranted to elucidate the underlying mechanisms and optimize patient care in the context of COVID-19-associated cardiovascular manifestations., (© 2024. The Author(s), under exclusive licence to The Author(s) under exclusive licence to The Japanese Society of Nuclear Medicine.)

Introduction: Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, existing detection methods for COVID-19 severity assessment often lack consideration of organs and tissues other than the lungs, which limits the accuracy and reliability of these predictive models., Material and Methods: The retrospective study included data from 515 COVID-19 patients (Cohort 1, n=415; Cohort 2, n=100) from two centers (Shanghai Public Health Center and Brazil Niteroi Hospital) between January 2020 and July 2020. Firstly, a three-stage EAT segmentation method was proposed by combining object detection and segmentation networks. Lung and EAT radiomics features were then extracted, and feature selection was performed. Finally, a hybrid model, based on seven machine learning models, was built for detecting COVID-19 severity. The hybrid model's performance and uncertainty were evaluated in both internal and external validation cohorts., Results: For EAT extraction, the Dice similarity coefficients (DSC) of the two centers were 0.972 (±0.011) and 0.968 (±0.005), respectively. For severity detection, the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) of the hybrid model increased by 0.09 (p&lt;0.001), 19.3 % (p&lt;0.05), and 18.0 % (p&lt;0.05) in the internal validation cohort, and by 0.06 (p&lt;0.001), 18.0 % (p&lt;0.05) and 18.0 % (p&lt;0.05) in the external validation cohort, respectively. Uncertainty and radiomics features analysis confirmed the interpretability of increased certainty in case prediction after inclusion of EAT features., Conclusion: This study proposed a novel three-stage EAT extraction method. We demonstrated that adding EAT radiomics features to a COVID-19 severity detection model results in increased accuracy and reduced uncertainty. The value of these features was also confirmed through feature importance ranking and visualization.

This study aimed to assess the severity and outcomes of COVID-19 in pregnant women, focusing on laboratory and radiological discrepancies between pregnant women and matched nonpregnant women. In this retrospective cross-sectional analysis, we matched 107 nonpregnant women with 66 pregnant women in terms of age, comorbidities, and the interval between symptom onset and hospital admission. Demographic, clinical, laboratory, and radiological data were collected, and chest CT scans were evaluated using a severity scale ranging from 0 to 5. Logistic regression and adjusted Cox regression models were used to assess the impact of various factors on pregnancy status and mortality rates. Differences in several laboratory parameters, including the neutrophil-to-lymphocyte ratio, liver aminotransferases, alkaline phosphatase, urea, triglycerides, cholesterol, HbA1c, ferritin, coagulation profiles, and blood gases, were detected. Radiologic exams revealed that nonpregnant women had sharper opacities, whereas pregnant women presented with hazy opacities and signs of crypt-organizing pneumonia. A notable difference was also observed in the pulmonary artery diameter. The mortality rate among pregnant women was 4.62%, which was comparable to the 5.61% reported in nonpregnant patients. Compared with nonpregnant patients, pregnancy did not significantly affect the severity or mortality of COVID-19. Our study revealed discernible differences in specific laboratory and imaging markers between pregnant and nonpregnant COVID-19 patients., (© 2024. The Author(s).)

Introduction: Although chest X-ray is commonly used to diagnose COVID-19 pneumonia, few studies have explored findings in pediatric patients. This study aimed to reveal chest X-ray characteristics in children with COVID-19 pneumonia and compare between non-severe and severe cases., Methods: This multicenter, nationwide retrospective study included all children aged 0 to 15 years who were admitted to 13 medical facilities throughout Thailand with COVID-19 pneumonia between January 2020 and October 2021. We analyzed the demographics, clinical features, and chest X-ray results of these children, and compared differences between the non-severe and severe groups., Results: During the study period, 1018 children (52% male, median age 5 years) were admitted with COVID-19 pneumonia. Most chest radiographic findings showed bilateral (51%) patchy/ground glass opacities (61%) in the central area (64%). Only 12% of the children exhibited typical classification for COVID-19 pneumonia, whereas 74% of chest radiographs were categorized as indeterminate. Comorbidities including chronic lung diseases [adjusted OR (95%CI): 14.56 (3.80-55.75), P-value <0.001], cardiovascular diseases [adjusted OR (95%CI): 7.54 (1.44-39.48), P-value 0.017], genetic diseases [adjusted OR (95%CI): 28.39 (4.55-177.23), P-value <0.001], clinical dyspnea [adjusted OR (95%CI): 12.13 (5.94-24.77), P-value <0.001], tachypnea [adjusted OR (95%CI): 3.92 (1.79-8.55), P-value 0.001], and bilateral chest X-ray infiltrations [adjusted OR (95%CI): 1.99 (1.05-3.78), P-value 0.036] were factors associated with severe COVID-19 pneumonia., Conclusion: Most children with COVID-19 pneumonia had indeterminate chest X-rays according to the previous classification. We suggest using chest X-rays in conjunction with clinical presentation to screen high-risk patients for early detection of COVID-19 pneumonia., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kulbun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: Little is known about the effect of SARS-CoV-2 infection on glioblastoma (GBM) growth, metabolism, and prognosis. Immunological changes within GBM tissue are potentially symptomatic, underlining the urgent need for a better understanding of this phenomenon. To date, the complex underlying biology has not been fully elucidated. A decisive role of the tumor microenvironment (TME) and the components of the immune system acting within it is assumed., Methods: Immunohistochemical staining of SARS-CoV-2 spike protein and immune cell infiltration of TME was performed on the tumor tissue of one patient. This patient developed hemiparesis 14 days after symptomatic SARS-CoV-2 infection, leading to tumor diagnosis. Subsequently and after biopsy, there was an unexpectedly good response to chemotherapy only. In looking for further evidence of the potential of SARS-CoV-2 to influence the course of GBM, two additional adult patients that had transient MRI changes and neurological deterioration following SARS-CoV-2 infection were evaluated., Results: In the patient for whom neurological deterioration in the course of SARS-CoV-2 led to GBM diagnosis, immunohistochemistry revealed virus-specific protein accumulation in the tumor cells, microglial activation, and the formation of T-cell nodules. In the other two patients, the findings were compatible with symptomatic pseudoprogression that occurred in a temporal relationship with SARS-CoV-2 infection., Conclusion: The results indicate a possible association between clinically relevant changes in GBM biology and SARS-CoV-2 infection, with histological confirmation of SARS-CoV-2-associated changes within the tumor tissue. The exact pathomechanism and underlying inflammatory pathways require further investigation., (© 2024. The Author(s).)

Thoracic ultrasound (TUS) is a tool that has become increasingly essential in the daily practice of thoracic medicine. Driven by the need to assess patients during the COVID-19 pandemic, there has been an increase in the use of point-of-care TUS, which has demonstrated several benefits, either as a complement to clinical decision-making for diagnosis or as a real-time guide for procedures, whether as a predictor or measure of treatment response. Here, we present a review of TUS, based on the most recent scientific evidence, from equipment and techniques to the fundamentals of pulmonary ultrasound, describing normal and pathological findings, as well as focusing on the management of lung disease and guidance for invasive thoracic procedures at the bedside. Finally, we highlight areas of perspective and potential lines of research to maintain interest in this valuable tool, in order to improve the diagnostic process and expand the treatment arsenal.

Objective: To analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs)., Methods: The case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. All individuals diagnosed with coronavirus disease 2019 (COVID-19) were confirmed via respiratory specimen viral nucleic acid testing. The included cases underwent CT, serum tumor marker testing and pathological examination. The diagnostic efficacy and clinical significance of CT, serum tumor marker testing and a combined test in identifying benign and malignant SPNs were analyzed using pathological histological findings as the gold standard. Finally, a nomogram mathematical model was established to predict the malignant probability of SPNs., Results: Of the 267 patients with SPNs, 91 patients were not afflicted with COVID-19, 36 exhibited malignant characteristics, whereas 55 demonstrated benign features. Conversely, within the cohort of 176 COVID-19 patients presenting with SPNs, 62 were identified as having malignant SPNs, and the remaining 114 were diagnosed with benign SPNs. CT scans revealed statistically significant differences between the benign and malignant SPNs groups in terms of CT values (P<0.001), maximum nodule diameter (P<0.001), vascular convergence sign (P<0.001), vacuole sign (P = 0.0007), air bronchogram sign (P = 0.0005), and lobulation sign (P = 0.0005). Malignant SPNs were associated with significantly higher levels of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) compared to benign SPNs (P < 0.05), while no significant difference was found in carbohydrate antigen 125 (CA125) levels (P = 0.054 for non-COVID-19; P = 0.072 for COVID-19). The sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) of the comprehensive diagnosis combining serum tumor markers and CT were significantly higher than those of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing alone (56.52%, 73.71%, 67.79%) (P < 0.05). A visual nomogram predictive model for malignant pulmonary nodules was constructed., Conclusion: Combining CT with testing for CEA, CA125, and NSE levels offers high diagnostic accuracy and sensitivity, enables precise differentiation between benign and malignant nodules, particularly in the context of COVID-19, thereby reducing the risk of unnecessary surgical interventions., (© 2024. The Author(s).)

COVID-19 is associated with increased risk for cognitive decline but very little is known regarding the neural mechanisms of this risk. We enrolled 49 adults (55% female, mean age = 30.7 ± 8.7), 25 with and 24 without a history of COVID-19 infection. We administered standardized tests of cognitive function and acquired brain connectivity data using MRI. The COVID-19 group demonstrated significantly lower cognitive function (W = 475, p < 0.001, effect size r = 0.58) and lower functional connectivity in multiple brain regions (mean t = 3.47 ±0.36, p = 0.03, corrected, effect size d = 0.92 to 1.5). Hypo-connectivity of these regions was inversely correlated with subjective cognitive function and directly correlated with fatigue (p < 0.05, corrected). These regions demonstrated significantly reduced local efficiency (p < 0.026, corrected) and altered effective connectivity (p < 0.001, corrected). COVID-19 may have a widespread effect on the functional connectome characterized by lower functional connectivity and altered patterns of information processing efficiency and effective information flow. This may serve as an adaptation to the pathology of SARS-CoV-2 wherein the brain can continue functioning at near expected objective levels, but patients experience lowered efficiency as brain fog., (© 2024. The Author(s).)

Introduction: Persistent post-COVID olfactory dysfunction continues to be studied due to the controversy in its pathophysiology and neuroimaging., Materials and Methods: The patients had confirmed mild COVID-19 infection with olfactory dysfunction of more than one month of evolution and they were compared to controls with normal olfaction, assessed using the Sniffin' Sticks Olfactory Test and underwent brain, magnetic resonance imaging (MRI) of the olfactory bulb and olfactory function., Results: A total of 8 patients and 2 controls participated. The average age of the patients was 34.5 years (SD 8.5), and that of the controls was 28.5 (SD 2.1). The average score in the patients' olfactory test was 7.9 points (SD 2.2). In brain and olfactory bulb MRI tests, no morphological differences were found. When evaluated by functional MRI, none of the patients activated the entorhinal area in comparison to the controls, who did show activation at this level. Activation of secondary olfactory areas in cases and controls were as follows: orbitofrontal (25% vs 100%), basal ganglia (25% vs 50%) and insula (38% vs 0%) respectively., Conclusions: There were no observed morphological changes in the brain MRI. Unlike the controls, none of the patients activated the entorhinal cortex in the olfactory functional MRI., (Copyright © 2024 Elsevier España, S.L.U. All rights reserved.)

Introduction and Objective: Multiple studies showed that patients with a severe course of COVID-19 may develop cardiovascular complications. Assessment of the incidence of myocardial injury in young, physically fit male patients with no comorbidities, and asymptomatic/mild course of the disease who recovered from COVID-19., Material and Methods: A prospective, single-center, observational cohort study of 75 young (median[IQR] age 22 years) physically fit male patients, without comorbidities and smoking who recently recovered from COVID-19. Results were compared with a control group of age-matched, physically fit men with no comorbidities who tested negative for SARS-CoV-2., Results: 19(25%) patients had possible COVID-19 related myocardial injury[PCRMI] on cardiovascular magnetic resonance [CMR] including definitive myocarditis (n=1;1.3%) and possible myocarditis (n=3;4%). Other abnormalities: mildly decreased (<50%) left ventricular(LV) ejection fraction (n=4;5%), increased LV end-diastolic volume index (n=8;11%) and LV mass index (n=9;12%). Patients with PCRMI had higher NT-pro-BNP level (29 vs 20pg/mL respectively, P=0.02) and lower LV ejection fraction (55% vs 59% respectively, P=0.03). PCRMI was demonstrated in 3(27%) volunteers from the control group based on the presence of LGE (2/18%) and decreased LV ejection fraction (1/9%). No volunteer from the control group was diagnosed with definitive or possible myocarditis., Conclusions: PCRMI was a frequent finding in young, asymptomatic, physically-fit patients sans comorbidities relatively late after recovery from COVID-19. Whereas no definitive or possible myocarditis was found in the control group, LGE was relatively frequent suggesting that our findings might not be COVID-19 specific. This warrants a need for further investigation into the long-term cardiovascular consequences of COVID-19.

Background: CT-scan and inflammatory and coagulation biomarkers could help in prognostication of COVID-19 in patients on ICU admission., Objective: The objectives of this study were to measure the prognostic value of the extent of lung parenchymal lesions on computed tomography (CT) and of several coagulation and inflammatory biomarkers, and to explore the characteristics of the patients depending on the extent of lung parenchymal lesions., Design: Retrospective monocentric observational study achieved on a dataset collected prospectively., Setting: Medical ICU of the university hospital of Clermont-Ferrand, France., Patients: All consecutive adult patients aged ≥18 years admitted between 20 March, 2020 and 31 August, 2021 for COVID-19 pneumonia., Interventions: Characteristics at baseline and during ICU stay, and outcomes at day 60 were recorded. The extent of lung parenchyma lesions observed on the chest CT performed on admission was established by artificial intelligence software., Measurements: Several clinical characteristics and laboratory features were collected on admission including plasma interleukin-6, HLA-DR monocytic-expression rate (mHLA-DR), and the extent of lung parenchymal lesions. Factors associated with day-60 mortality were investigated by uni- and multivariate survival analyses., Results: 270 patients were included. Inflammation biomarkers including the levels of neutrophils, CRP, ferritin and Il10 were the indices the most associated with the severity of the extent of the lung lesions. Patients with more extensive lung parenchymal lesions (≥ 75%) on admission had higher CRP serum levels. The extent of lung parenchymal lesions was associated with a decrease in the PaO2/FiO2 ratio(p<0.01), fewer ventilatory-free days (p = 0.03), and a higher death rate at day 60(p = 0.01). Extent of the lesion of more than 75% was independently associated with day-60 mortality (aHR = 1.72[1.06; 2.78], p = 0.03). The prediction of death at day 60 was improved when considering simultaneously biological and radiological markers obtained on ICU admission (AUC = 0.78)., Conclusions: The extent of lung parenchyma lesions on CT was associated with inflammation, and the combination of coagulation and inflammatory biomarkers and the extent of the lesions predicted the poorest outcomes., Competing Interests: no competing of interest, (Copyright: © 2024 Krisht et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Orthopantomography (OPG) is a routine imaging method in dental practice and an essential di- agnostic tool in dentistry. However, OPGs are challenging to interpret due to many overlapping structures. Graduates of dental schools should be aware of image distortions caused by various factors and be able to distinguish them from typical structures to make an accurate diagnosis. The aim was to determine the correlation between the knowledge regarding the location of craniofacial structures of the 1st through 3rd- year dental students and the ability to recognized them on OPGs. The study was conducted in 2021 on 131 dental students using the Microsoft Teams program. Each participant had to determine the location of 4 anthropometric points on 4 OPGs. Using proprietary software, the authors determined the Articular angle between them. The researchers performed the statistical analysis. The Bioethics Committee approved the research. The results of students from particular years of studies did not show statistical significance. There was no statistically significant difference between males and females. Only the answers from third-year male students regarding the one Articular angle showed statistically significant differences compared to the rest of the participants. Recognizing joint structures on OPG is very important from the clinical point of view, although it is often overlooked in diagnostics. Students in the first years of dental school acquire the ability to assess and analyze TMJ on OPGs properly. OPGs have many advantages, including high availability, low cost and low radiation dose. In conclusion we can say that in the first three years of studies, dental students' image assessment skills in the craniofacial radiology field remain the same.

Background: Frequent chest CTs within a short period during follow-up of long COVID patients may increase the risk of radiation-related health effects in the exposed individuals. We aimed to assess the image quality and diagnostic accuracy of ultra-low-dose CT (ULDCT) chest compared to standard-dose CT (SDCT) in detecting lung abnormalities associated with long COVID., Methods: In this prospective study, 100 long COVID patients with respiratory dysfunction underwent SDCT and ULDCT chest that were compared in terms of objective (signal-to-noise ratio, SNR) and subjective image quality (image graininess, sharpness, artifacts, and diagnostic accuracy along with the European guidelines on image quality criteria for CT chest), detection of imaging patterns of long COVID, CT severity score, and effective radiation dose. Additionally, the diagnostic performance of ULDCT was compared among obese (BMI≥30 kg/m 2 ) and non-obese (BMI<30 kg/m 2 ) subjects., Results: The mean age of study participants was 53 ± 12.9 years, and 68% were male. The mean SNR was 31.4 ± 5.5 and 11.3 ± 4.6 for SDCT and ULDCT respectively (p< 0.0001). Common findings seen on SDCT included ground-glass opacities (GGOs, 77%), septal thickening/reticulations (67%), atelectatic/parenchymal bands (63%) and nodules (26%). ULDCT provided sharp images, with no/minimal graininess, and high diagnostic confidence in 81%, 82% and 80% of the cases respectively. The sensitivity of ULDCT for various patterns of long COVID was 72.7% (GGOs), 71.6% (interlobular septal thickening/reticulations), 100% (consolidation), 81% (atelectatic/parenchymal bands) and 76.9% (nodules). ULDCT scans in non-obese subjects exhibited a significantly higher sensitivity (88% vs. 60.3%, p < 0.0001) and diagnostic accuracy (97.7% vs. 84.9%, p < 0.0001) compared to obese subjects. ULDCT showed very strong correlation with SDCT in terms of CT severity score (r = 0.996, p < 0.0001). The mean effective radiation dose with ULDCT was 0.25 ± 0.02 mSv with net radiation dose reduction of 94.8% ± 1.7% (p < 0.0001) when compared to SDCT (5.5 ± 1.96 mSv)., Conclusion: ULDCT scans achieved comparable diagnostic accuracy to SDCT for detecting long COVID lung abnormalities in non-obese patients, while significantly reducing radiation exposure., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Devkota, Garg, Debi, Dhooria, Dua, Prabhakar, Soni, Maralakunte, Gulati, Singh and Sandhu.)

Pneumonia caused by novel coronavirus is an acute respiratory infectious disease. Its rapid spread in a short period of time has brought great challenges for global public health. The use of deep learning and radiomics methods can effectively distinguish the subtypes of lung diseases, provide better clinical prognosis accuracy, and assist clinicians, enabling them to adjust the clinical management level in time. The main goal of this study is to verify the performance of deep learning and radiomics methods in the classification of COVID-19 lesions and reveal the image characteristics of COVID-19 lung disease. An MFPN neural network model was proposed to extract the depth features of lesions, and six machine-learning methods were used to compare the classification performance of deep features, key radiomics features and combined features for COVID-19 lung lesions. The results show that in the COVID-19 image classification task, the classification method combining radiomics and deep features can achieve good classification results and has certain clinical application value.

Background and Aim: We witnessed a sharp peak in the incidence of invasive fungal sinusitis, particularly mucormycosis, in patients with history of coronavirus disease 2019 (COVID-19) infection in India. Rhino-orbito-cerebral mucormycosis (ROCM) is a fulminant rapidly progressive disease. Early diagnosis significantly improves patient survival and outcomes. Hence, neuroimaging plays a very important role. We studied the magnetic resonance (MR) imaging manifestations of invasive fungal sinusitis and established an imaging protocol, which helps in early diagnosis of the disease per se as well as its complications. We evaluated the differences between COVID-19-associated and non-COVID-19-associated ROCM., Materials and Methods: We retrospectively analyzed the MR imaging manifestations of 91 histopathologically proven cases of post-COVID-19-invasive fungal sinusitis., Results: We observed stage I disease limited to sinuses in 25.2%, stage II disease with intraorbital spread in 23%, and stage III disease with intracranial spread in 51.6% of our patients. Dural involvement was the commonest and earliest sign of stage III disease. Direct parenchymal invasion from the adjacent paranasal sinuses was the commonest pattern of cerebral involvement, involving basifrontal lobe (14.2%) followed by anteromedial temporal lobe (5.4%). We observed orbital and intracranial complications including subperiosteal orbital abscess (1%), cavernous sinus involvement (29.6%), angioinvasion (15.3%), perineural spread (9.8%), and osteomyelitis of skull base and craniofacial bones (45%). Contrary to non-COVID-19-associated ROCM, we did not observe any case with superior ophthalmic vein/dural venous sinus thrombosis or basilar artery angioinvasion in our study., Conclusions: In our study, stage III disease was most commonly due to direct parenchymal invasion into frontal and temporal lobes from the adjacent frontal and sphenoid sinuses, respectively. The commonest vascular complications in our study were cavernous sinus involvement followed by angioinvasion into the cavernous ICA leading to watershed infarcts., (Copyright © 2024 Copyright: © 2024 Neurology India, Neurological Society of India.)

Introduction: There is still a lack of clinical evidence comprehensively evaluating the effectiveness of antiviral treatments for COVID-19 hospitalized patients., Methods: A retrospective cohort study was conducted at Beijing You'An Hospital, focusing on patients treated with nirmatrelvir/ritonavir or azvudine. The study employed a tripartite analysis-viral dynamics, survival curve analysis, and AI-based radiological analysis of pulmonary CT images-aiming to assess the severity of pneumonia., Results: Of 370 patients treated with either nirmatrelvir/ritonavir or azvudine as monotherapy, those in the nirmatrelvir/ritonavir group experienced faster viral clearance than those treated with azvudine (5.4 days vs. 8.4 days, p < 0.001). No significant differences were observed in the survival curves between the two drug groups. AI-based radiological analysis revealed that patients in the nirmatrelvir group had more severe pneumonia conditions (infection ratio is 11.1 vs. 5.35, p = 0.007). Patients with an infection ratio higher than 9.2 had nearly three times the mortality rate compared to those with an infection ratio lower than 9.2., Conclusions: Our study suggests that in real-world studies regarding hospitalized patients with COVID-19 pneumonia, the antiviral effect of nirmatrelvir/ritonavir is significantly superior to azvudine, but the choice of antiviral agents is not necessarily linked to clinical outcomes; the severity of pneumonia at admission is the most important factor to determine prognosis. Additionally, our findings indicate that pulmonary AI imaging analysis can be a powerful tool for predicting patient prognosis and guiding clinical decision-making., (© 2024 The Author(s). Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)

Purpose To develop an artificial intelligence model that uses supervised contrastive learning (SCL) to minimize bias in chest radiograph diagnosis. Materials and Methods In this retrospective study, the proposed method was evaluated on two datasets: the Medical Imaging and Data Resource Center (MIDRC) dataset with 77 887 chest radiographs in 27 796 patients collected as of April 20, 2023, for COVID-19 diagnosis and the National Institutes of Health ChestX-ray14 dataset with 112 120 chest radiographs in 30 805 patients collected between 1992 and 2015. In the ChestX-ray14 dataset, thoracic abnormalities included atelectasis, cardiomegaly, effusion, infiltration, mass, nodule, pneumonia, pneumothorax, consolidation, edema, emphysema, fibrosis, pleural thickening, and hernia. The proposed method used SCL with carefully selected positive and negative samples to generate fair image embeddings, which were fine-tuned for subsequent tasks to reduce bias in chest radiograph diagnosis. The method was evaluated using the marginal area under the receiver operating characteristic curve difference (∆mAUC). Results The proposed model showed a significant decrease in bias across all subgroups compared with the baseline models, as evidenced by a paired t test ( P < .001). The ∆mAUCs obtained by the proposed method were 0.01 (95% CI: 0.01, 0.01), 0.21 (95% CI: 0.21, 0.21), and 0.10 (95% CI: 0.10, 0.10) for sex, race, and age subgroups, respectively, on the MIDRC dataset and 0.01 (95% CI: 0.01, 0.01) and 0.05 (95% CI: 0.05, 0.05) for sex and age subgroups, respectively, on the ChestX-ray14 dataset. Conclusion Employing SCL can mitigate bias in chest radiograph diagnosis, addressing concerns of fairness and reliability in deep learning-based diagnostic methods. Keywords: Thorax, Diagnosis, Supervised Learning, Convolutional Neural Network (CNN), Computer-aided Diagnosis (CAD) Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Johnson in this issue.

Rationale and Objectives: It remains difficult to predict longitudinal outcomes in long-COVID, even with chest CT and functional MRI. 129 Xe MRI reflects airway dysfunction, measured using ventilation defect percent (VDP) and in long-COVID patients, MRI VDP was abnormal, suggestive of airways disease. While MRI VDP and quality-of-life improved 15-month post-COVID infection, both remained abnormal. To better understand the relationship of airways disease and quality-of-life improvements in patients with long-COVID, we extracted 129 Xe ventilation MRI textures and generated machine-learning models in an effort to predict improved quality-of-life, 15-month post-infection., Materials and Methods: Long-COVID patients provided written-informed consent to 3-month and 15-month post-infection visits. Pyradiomics was used to extract 129 Xe ventilation MRI texture features, which were ranked using a Random-Forest classifier. Top-ranking features were used in classification models to dichotomize patients based on St. George's Respiratory Questionnaire (SGRQ) score improvement greater than the minimal-clinically-important-difference (MCID). Classification performance was evaluated using the area under the receiver-operator-characteristic-curve (AUC), sensitivity, and specificity., Results: 120 texture features were extracted from 129 Xe ventilation MRI in 44 long-COVID participants (54 ± 14 years), including 30 (52 ± 12 years) with ΔSGRQ≥MCID and 14 (58 ± 18 years) with ΔSGRQ129 Xe MRI ventilation textures explained improved SGRQ-scores 12 months later, and outperformed clinical models. Their unique spatial-intensity information helps build our understanding about long-COVID airway dysfunction., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Grace Parraga reports financial support was provided by Ontario Ministry of Health and Long-Term Care. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Abstract: A 76-year-old woman with liver and bone metastasis of a duodenal neuroendocrine tumor received peptide receptor radionuclide therapy with 177 Lu-DOTATATE. Scintigraphy with SPECT/CT performed 4 days after the treatment demonstrated 177 Lu-DOTATATE uptake as multifocal ground glass opacities in the bilateral lungs. This uptake was considered to be due to COVID-19 pneumonia because the patient was infected with the virus 7 days prior to the treatment. The lung opacities became smaller, showing a decreased uptake, 2 months later, after the second treatment. 177 Lu-DOTATATE may be taken up during the active phase of COVID-19 pneumonia., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Pulmonary Embolism (PE) has diverse manifestations with different etiologies such as venous thromboembolism, septic embolism, and paradoxical embolism. In this study, a novel attention-based multi-task model is proposed for PE segmentation and detection from Computed Tomography Pulmonary Angiography (CTPA) images. A Y-Net architecture is used to implement this model, which facilitates segmentation and classification jointly, improving performance and efficiency. It is leveraged with Multi Head Attention (MHA), which allows the model to focus on important regions of the image while suppressing irrelevant information, improving the accuracy of the segmentation and detection tasks. The proposed PE-YNet model is tested with two public datasets, achieving a maximum mean detection and segmentation accuracy of 99.89% and 99.83%, respectively, on the CAD-PE challenge dataset. Similarly, it also achieves a detection accuracy of 99.75% and a segmentation accuracy of 99.81% on the FUMPE dataset. Additionally, sensitivity analysis also shows a high sensitivity of 0.9885 for the localization error ɛ = 0 for the CAD-PE dataset, demonstrating the model's robustness against false predictions compared to state-of-the-art models. Further, this model also exhibits lower inference time, size, and memory usage compared to representative models. An automated PE-YNet tool can assist physicians with PE diagnosis, treatment, and prognosis monitoring in the clinical management of CoVID-19., (© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)

Purpose: A hypometabolic profile involving the limbic areas, brainstem and cerebellum has been identified in long COVID patients using [ 18 F]fluorodeoxyglucose (FDG)-PET. This study was conducted to evaluate possible recovery of brain metabolism during the follow-up of patients with prolonged symptoms., Methods: Fifty-six adults with long COVID who underwent two brain [ 18 F]FDG-PET scans in our department between May 2020 and October 2022 were retrospectively analysed, and compared to 51 healthy subjects. On average, PET1 was performed 7 months (range 3-17) after acute COVID-19 infection, and PET2 was performed 16 months (range 8-32) after acute infection, because of persistent severe or disabling symptoms, without significant clinical recovery. Whole-brain voxel-based analysis compared PET1 and PET2 from long COVID patients to scans from healthy subjects (p-voxel < 0.001 uncorrected, p-cluster < 0.05 FWE-corrected) and PET1 to PET2 (with the same threshold, and secondarily with a less constrained threshold of p-voxel < 0.005 uncorrected, p-cluster < 0.05 uncorrected). Additionally, a region-of-interest (ROI) semiquantitative anatomical approach was performed for the same comparisons (p < 0.05, corrected)., Results: PET1 and PET2 revealed voxel-based hypometabolisms consistent with the previously reported profile in the literature. This between-group analysis comparing PET1 and PET2 showed minor improvements in the pons and cerebellum (8.4 and 5.2%, respectively, only significant under the less constrained uncorrected p-threshold); for the pons, this improvement was correlated with the PET1-PET2 interval (r = 0.21, p < 0.05). Of the 14,068 hypometabolic voxels identified on PET1, 6,503 were also hypometabolic on PET2 (46%). Of the 7,732 hypometabolic voxels identified on PET2, 6,094 were also hypometabolic on PET1 (78%). The anatomical ROI analysis confirmed the brain hypometabolism involving limbic region, the pons and cerebellum at PET1 and PET2, without significant changes between PET1 and PET2., Conclusion: Subjects with persistent symptoms of long COVID exhibit durable deficits in brain metabolism, without progressive worsening., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The actual role of coronavirus disease 2019 (COVID-19) in brain damage has been increasingly reported, necessitating a meta-analysis to collate and summarize the inconsistent findings from functional imaging and voxel-based morphometry (VBM) studies. A comprehensive voxel-wise meta-analysis of the whole brain was conducted to identify alterations in functional activity and gray matter volume (GMV) between COVID-19 patients and healthy controls (HCs) by using Seed-based d Mapping software. We included 15 functional imaging studies (484 patients with COVID-19, 534 HCs) and 9 VBM studies (449 patients with COVID-19, 388 HCs) in the analysis. Overall, patients with COVID-19 exhibited decreased functional activity in the right superior temporal gyrus (STG) (extending to the right middle and inferior temporal gyrus, insula, and temporal pole [TP]), left insula, right orbitofrontal cortex (OFC) (extending to the right olfactory cortex), and left cerebellum compared to HCs. For VBM, patients with COVID-19, relative to HCs, showed decreased GMV in the bilateral anterior cingulate cortex/medial prefrontal cortex (extending to the bilateral OFC), and left cerebellum, and increased GMV in the bilateral amygdala (extending to the bilateral hippocampus, STG, TP, MTG, and right striatum). Moreover, overlapping analysis revealed that patients with COVID-19 exhibited both decreased functional activity and increased GMV in the right TP (extending to the right STG). The multimodal meta-analysis suggests that brain changes of function and structure in the temporal lobe, OFC and cerebellum, and functional or structural alterations in the insula and the limbic system in COVID-19. These findings contribute to a better understanding of the pathophysiology of brain alterations in COVID-19. SIGNIFICANCE STATEMENT: This first large-scale multimodal meta-analysis collates existing neuroimaging studies and provides voxel-wise functional and structural whole-brain abnormalities in COVID-19. Findings of this meta-analysis provide valuable insights into the dynamic brain changes (from infection to recovery) and offer further explanations for the pathophysiological basis of brain alterations in COVID-19., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: To investigate the prevalence of cerebrovascular MRI markers in unselected patients hospitalized for COVID-19 (Coronavirus disease 2019), we compared these with healthy controls without previous SARS-CoV-2 infection or hospitalization and subsequently, investigated longitudinal (incidental) lesions in patients after three months., Methods: CORONIS (CORONavirus and Ischemic Stroke) was an observational cohort study in adult hospitalized patients for COVID-19 and controls without COVID-19, conducted between April 2021 and September 2022. Brain MRI was performed shortly after discharge and after 3 months. Outcomes included recent ischemic (DWI-positive) lesions, previous infarction, microbleeds, white matter hyperintensities (WMH) and intracerebral hemorrhage and were analysed with logistic regression to adjust for confounders., Results: 125 patients with COVID-19 and 47 controls underwent brain MRI a median of 41.5 days after symptom onset. DWI-positive lesions were found in one patient (1%) and in one (2%) control, both clinically silent. WMH were more prevalent in patients (78%) than in controls (62%) (adjusted OR: 2.95 [95% CI: 1.07-8.57]), other cerebrovascular MRI markers did not differ. Prevalence of markers in ICU vs. non-ICU patients was similar. After three months, five patients (5%) had new cerebrovascular lesions, including DWI-positive lesions (1 patient, 1.0%), cerebral infarction (2 patients, 2.0%) and microbleeds (3 patients, 3.1%)., Conclusion: Overall, we found no higher prevalence of cerebrovascular markers in unselected hospitalized COVID-19 patients compared to controls. The few incident DWI-lesions were most likely to be explained by risk-factors of small vessel disease. In the general hospitalized COVID-19 population, COVID-19 shows limited impact on cerebrovascular MRI markers shortly after hospitalization., (© 2024. The Author(s).)

Aim: The challenges posed by the assessment of elderly trauma patients increase the reliance on emergency CT scanning to diagnose an injury. The COVID-19 pandemic posed challenges to image service delivery. We sought to assess the effect of the COVID-19 pandemic on the imaging of elderly trauma., Materials and Methods: All trauma patients aged 65 and over who underwent whole-body CT scanning in the same 3-month period (April-June) in 2019 and 2022 were included in our study. Data was collected on demographics, time of scanning, clinical request details and positive report findings. Anatomical injury distribution, abbreviated injury scale (AIS) and injury severity scores (ISS) were calculated. Consensus judgment was obtained on scan justification and significance of findings. Chi-squared test of association was applied to the categorical outcomes of interest. Associations were considered significant if p<0.05., Results: 79 patients were scanned in the pre-pandemic assessment period versus 217 post-pandemic, an increase of 175%, including a 248% rise in fall-from-standing requests. There was a statistically significant reduction of trauma CT requests meeting fulfilment criteria post-pandemic (95% vs 83%) (p=0.008), with significantly fewer positive findings (45.6% vs 29%) (p=0.024). There was a decrease in median ISS score in the post-pandemic group (p=0.062)., Conclusion: Despite increased scanning, there was a reduction in both the number and severity of positive traumatic imaging findings. The pandemic has increased reliance on CT without a concomitant increase in the detection of positive findings nor clinically significant findings. This single-centre study demonstrates the need to further evaluate pan-CT trauma scanning in silver trauma patients., (Copyright © 2024 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

Background: Coronavirus disease 2019 (COVID-19) has significantly impacted National Collegiate Athletic Association (NCAA) athletics, with specific concerns for cardiac involvement after infection. Pericardial abnormalities have been seen in up to 39.5% of athletes after COVID-19 infection, while myocardial involvement has been reported at a lower rate of 2.7%. To date, myocardial injury has been seen in 0.6% to 0.7% of athletes when using symptom screening and imaging as clinically indicated, which increases to 2.3% to 3.0% when all athletes with COVID-19 undergo cardiac magnetic resonance (CMR) imaging., Purpose: This study will examine whether there exists an ideal time from positive COVID-19 results to obtaining imaging to increase the likelihood of finding abnormalities., Study Design: Prospective cohort study., Level of Evidence: Level 3., Methods: NCAA athletes at West Virginia University who were found to be COVID-19 positive on routine screening were required to undergo echocardiography (ECG) and CMR. These data were reviewed by cardiology and determined to be normal or abnormal. Statistical analysis with logistic regression and descriptive statistics was performed to evaluate whether a time existed where abnormalities on imaging were most likely to be found., Results: A total of 41 athletes were included in this study. ECG was performed earlier on average than CMR imaging, at 18.2 days versus 27.5 days. No significant difference was found in timing from COVID-19 infection diagnosis and abnormalities seen on imaging for either ECG or CMR imaging., Conclusion: The risk of cardiac involvement in athletes in the setting of COVID-19 has already been documented. This study suggests that imaging timing is independent of cardiac involvement with no correlation to specific time periods where more abnormalities may be found. However, CMR imaging showing changes at day 54 after infection suggests cardiac findings can be seen months after imaging., Clinical Relevance: Cardiac imaging for athletes after contracting COVID-19 does not show a significant relationship to time of imaging. However, given the cardiac involvement seen months after diagnosis, further examination of prolonged cardiac effects must be carried out., Competing Interests: The authors report no potential conflicts of interest in the development and publication of this article.

COVID-19 pneumonia severity assessment is of great clinical importance, and lung ultrasound (LUS) plays a crucial role in aiding the severity assessment of COVID-19 pneumonia due to its safety and portability. However, its reliance on qualitative and subjective observations by clinicians is a limitation. Moreover, LUS images often exhibit significant heterogeneity, emphasizing the need for more quantitative assessment methods. In this paper, we propose a knowledge fused latent representation framework tailored for COVID-19 pneumonia severity assessment using LUS examinations. The framework transforms the LUS examination into latent representation and extracts knowledge from regions labeled by clinicians to improve accuracy. To fuse the knowledge into the latent representation, we employ a knowledge fusion with latent representation (KFLR) model. This model significantly reduces errors compared to approaches that lack prior knowledge integration. Experimental results demonstrate the effectiveness of our method, achieving high accuracy of 96.4 % and 87.4 % for binary-level and four-level COVID-19 pneumonia severity assessments, respectively. It is worth noting that only a limited number of studies have reported accuracy for clinically valuable exam level assessments, and our method surpass existing methods in this context. These findings highlight the potential of the proposed framework for monitoring disease progression and patient stratification in COVID-19 pneumonia cases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Radiomics is an innovative field in Personalized Medicine to help medical specialists in diagnosis and prognosis. Mainly, the application of Radiomics to medical images requires the definition and delimitation of the Region Of Interest (ROI) on the medical image to extract radiomic features. The aim of this preliminary study is to define an approach that automatically detects the specific areas indicative of a particular disease and examines them to minimize diagnostic errors associated with false positives and false negatives. This approach aims to create a nxn grid on the DICOM image sequence and each cell in the matrix is associated with a region from which radiomic features can be extracted. The proposed procedure uses the Model Checking technique and produces as output the medical diagnosis of the patient, i.e., whether the patient under analysis is affected or not by a specific disease. Furthermore, the matrix-based method also localizes where appears the disease marks. To evaluate the performance of the proposed methodology, a case study on COVID-19 disease is used. Both results on disease identification and localization seem very promising. Furthermore, this proposed approach yields better results compared to methods based on the extraction of features using the whole image as a single ROI, as evidenced by improvements in Accuracy and especially Recall. Our approach supports the advancement of knowledge, interoperability and trust in the software tool, fostering collaboration among doctors, staff and Radiomics., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Antonella Santone reports financial support was provided by Ministry of Education and Merit and by Ministry of University and Research. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

COVID-19 detection using chest X-rays (CXR) has evolved as a significant method for early diagnosis of the pandemic disease. Clinical trials and methods utilize X-ray images with computer and intelligent algorithms to improve detection and classification precision. This article thus proposes a fuzzy-based adaptive convolution neural network (FACNN) model to improve the detection precision by confining the false rates. The feature extraction process between the successive regions is validated using a fuzzy process that classifies labeled and unknown pixels. The membership functions are derived based on high precision features for detection and false rate suppression process. The convolution neural network process is responsible for increasing detection precision through recurrent training based on feature availability. This availability analysis is verified using fuzzy derivatives under local variances. Based on variance-reduced features, the appropriate regions with labeled and unknown features are used for normal or infected classification. Thus, the proposed FACNN improves accuracy, precision, and feature extraction by 14.36%, 8.74%, and 12.35%, respectively. This model reduces the false rate and extraction time by 10.35% and 10.66%, respectively., (© 2024. International Federation for Medical and Biological Engineering.)

Purpose: To demonstrate the feasibility of zigzag sampling for 3D rapid hyperpolarized 129 Xe ventilation MRI in human., Methods: Zigzag sampling in one direction was combined with gradient-recalled echo sequence (GRE-zigzag-Y) to acquire hyperpolarized 129 Xe ventilation images. Image quality was compared with a balanced SSFP (bSSFP) sequence with the same spatial resolution for 12 healthy volunteers (HVs). For another 8 HVs and 9 discharged coronavirus disease 2019 subjects, isotropic resolution 129 Xe ventilation images were acquired using zigzag sampling in two directions through GRE-zigzag-YZ. 129 Xe ventilation defect percent (VDP) was quantified for GRE-zigzag-YZ and bSSFP acquisitions. Relationships and agreement between these VDP measurements were evaluated using Pearson correlation coefficient (r) and Bland-Altman analysis., Results: For 12 HVs, GRE-zigzag-Y and bSSFP required 2.2 s and 10.5 s, respectively, to acquire 129 Xe images with a spatial resolution of 3.96 × 3.96 × 10.5 mm 3 . Structural similarity index, mean absolute error, and Dice similarity coefficient between the two sets of images and ventilated lung regions were 0.85 ± 0.03, 0.0015 ± 0.0001, and 0.91 ± 0.02, respectively. For another 8 HVs and 9 coronavirus disease 2019 subjects, 129 Xe images with a nominal spatial resolution of 2.5 × 2.5 × 2.5 mm 3 were acquired within 5.5 s per subject using GRE-zigzag-YZ. VDP provided by GRE-zigzag-YZ was strongly correlated (R 2  = 0.93, p < 0.0001) with that generated by bSSFP with minimal biases (bias = -0.005%, 95% limit-of-agreement = [-0.414%, 0.424%])., Conclusion: Zigzag sampling combined with GRE sequence provides a way for rapid 129 Xe ventilation imaging., (© 2024 International Society for Magnetic Resonance in Medicine.)

We propose a shape prior representation-constrained multi-scale features fusion segmentation network for medical image segmentation, including training and testing stages. The novelty of our training framework lies in two modules comprised of the shape prior constraint and the multi-scale features fusion. The shape prior learning model is embedded into a segmentation neural network to solve the problems of low contrast and neighboring organs with intensities similar to the target organ. The latter can provide both local and global contexts to address the issues of large variations in patient postures as well as organ's shape. In the testing stage, we propose a circular collaboration framework strategy which combines a shape generator auto-encoder network model with a segmentation network model, allowing the two models to collaborate with each other, resulting in a cooperative effect that leads to accurate segmentations. Our proposed method is evaluated and demonstrated on the ACDC MICCAI'17 Challenge Dataset, CT scans datasets, namely, in COVID-19 CT lung, and LiTS2017 liver from three different datasets, and its results are compared with the recent state of the art in these areas. Our method ranked 1st on the ACDC Dataset in terms of Dice score and achieved very competitive performance on COVID-19 CT lung and LiTS2017 liver segmentation., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Yuanzhi Cheng reports financial support was provided by Japan Society for the Promotion of Science. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to inlfuence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)