MRI of cardiac involvement in COVID-19.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to a diverse pattern of myocardial injuries, including myocarditis, which is linked to adverse outcomes in patients. Research indicates that myocardial injury is associated with higher mortality in hospitalized severe COVID-19 patients (75.8% vs 9.7%). Cardiovascular Magnetic Resonance (CMR) has emerged as a crucial tool in diagnosing both ischaemic and non-ischaemic myocardial injuries, providing detailed insights into the impact of COVID-19 on myocardial tissue and function. This review synthesizes existing studies on the histopathological findings and CMR imaging patterns of myocardial injuries in COVID-19 patients. CMR imaging has revealed a complex pattern of cardiac damage in these patients, including myocardial inflammation, oedema, fibrosis, and ischaemic injury, due to coronary microthrombi. This review also highlights the role of LLC criteria in diagnosis of COVID-related myocarditis and the importance of CMR in detecting cardiac complications of COVID-19 in specific groups, such as children, manifesting multisystem inflammatory syndrome in children (MIS-C) and athletes, as well as myocardial injuries post-COVID-19 infection or following COVID-19 vaccination. By summarizing existing studies on CMR in COVID-19 patients and highlighting ongoing research, this review contributes to a deeper understanding of the cardiac impacts of COVID-19. It emphasizes the effectiveness of CMR in assessing a broad spectrum of myocardial injuries, thereby enhancing the management and prognosis of patients with COVID-19 related cardiac complications.

5-fluorouracil-induced coronary vasospasm: A cardiovascular magnetic resonance imaging case report.

We report the case of a 45-year old male with metastatic colon cancer who presented with chest pain and transient diffuse ST-segment elevation on electrocardiogram after his third cycle of FOLFOX (folinic acid, fluorouracil, oxaliplatin). Initial transthoracic echocardiogram showed reduced left ventricular ejection fraction of 35% with mildly elevated troponins. Further investigations with cardiovascular magnetic resonance imaging demonstrated recovery of left ventricular function with evidence suggestive of coronary vasospasm. This case report will review the utility of cardiovascular magnetic imaging in the evaluation of underlying etiologies for myocardial injury in patients with low likelihood of obstructive coronary artery disease.

Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans.

The main objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on volumetric chest CT scans, which are acquired in different imaging centers using different scanners and technical settings. We demonstrated that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, it performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. More specifically, we extracted the subset of the test images for which the model generated a confident prediction and used the extracted subset along with the training set to retrain and update the benchmark model (the model trained on the initial train set). Finally, we adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a single scanning protocol and standard radiation dose. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low-dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. This dataset is referred to as the "SPGC-COVID" dataset. The entire test dataset used in this study contains 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate that our proposed framework performs well on all test sets achieving total accuracy of 96.15% (95%CI: [91.25-98.74]), COVID-19 sensitivity of 96.08% (95%CI: [86.54-99.5]), CAP sensitivity of 92.86% (95%CI: [76.50-99.19]), Normal sensitivity of 98.04% (95%CI: [89.55-99.95]) while the confidence intervals are obtained using the significance level of 0.05. The obtained AUC values (One class vs Others) are 0.993 (95%CI: [0.977-1]), 0.989 (95%CI: [0.962-1]), and 0.990 (95%CI: [0.971-1]) for COVID-19, CAP, and Normal classes, respectively. The experimental results also demonstrate the capability of the proposed unsupervised enhancement approach in improving the performance and robustness of the model when being evaluated on varied external test sets.

Pancreatic involvement in Erdheim-Chester disease: Rare presentation of a rare disease.

Erdheim-Chester disease (ECD) as a rare non-Langerhans histiocytosis has various clinical manifestations. It is characterized histologically by infiltration of every organ, more commonly bone, retroperitoneum, cardiovascular and CNS systems with foamy, lipid -laden macrophage. Pancreatic involvement as a manifestation of this uncommon disease has very rarely been reported. Here we report a 73-year-old woman with ECD and pancreas involvement in CT, MRI and PET scans. We also aim to increase radiologist knowledge about considering ECD as a differential diagnosis for pancreas mass in the appropriate clinical situation.

Prognosis of Spontaneous Pneumothorax/Pneumomediastinum in Coronavirus Disease 2019: The CoBiF Score.

Objectives: Pneumothorax and pneumomediastinum are associated with high mortality in invasively ventilated coronavirus disease 2019 (COVID-19) patients; however, the mortality rates among non-intubated patients remain unknown. We aimed to analyze the clinical features of COVID-19-associated pneumothorax/pneumomediastinum in non-intubated patients and identify risk factors for mortality. Methods: We searched PubMed Scopus and Embase from January 2020 to December 2021. We performed a pooled analysis of 151 patients with no invasive mechanical ventilation history from 17 case series and 87 case reports. Subsequently, we developed a novel scoring system to predict in-hospital mortality; the system was further validated in multinational cohorts from ten countries (n = 133). Results: Clinical scenarios included pneumothorax/pneumomediastinum at presentation (n = 68), pneumothorax/pneumomediastinum onset during hospitalization (n = 65), and pneumothorax/pneumomediastinum development after recent COVID-19 treatment (n = 18). Significant differences were not observed in clinical outcomes between patients with pneumomediastinum and pneumothorax (±pneumomediastinum). The overall mortality rate of pneumothorax/pneumomediastinum was 23.2%. Risk factor analysis revealed that comorbidities bilateral pneumothorax and fever at pneumothorax/pneumomediastinum presentation were predictors for mortality. In the new scoring system, i.e., the CoBiF system, the area under the curve which was used to assess the predictability of mortality was 0.887. External validation results were also promising (area under the curve: 0.709). Conclusions: The presence of comorbidity bilateral pneumothorax and fever on presentation are significantly associated with poor prognosis in COVID-19 patients with spontaneous pneumothorax/pneumomediastinum. The CoBiF score can predict mortality in clinical settings as well as simplify the identification and appropriate management of patients at high risk.

MRI findings of epipericardial fat necrosis: As a rare cause of acute chest pain in a healthy man.

Epipericardial fat necrosis (EPFN) is a rare, benign cause of acute chest pain imitating symptoms of life-threatening diseases, such as acute coronary syndrome. Here We report a 37-year-old, healthy male presented to the emergency department (ED) with sudden-onset pleuritic chest pain after an isometric physical training. Initial cardiac workup included ECG, echocardiography was unremarkable, but diagnosis of an inflammatory process that involved the epipericardial fat tissue surrounding the heart was made by showing encapsulated fatty lesion, enhanced adjacent parietal pericardium using of contrast-enhanced magnetic resonance imaging (MRI). Magnetic resonance imaging would help physicians to differentiate EPFN from severe and life-treating conditions.

Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network.

Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. Based on a cross validation, the AI model achieves COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text], and accuracy of [Formula: see text]. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text] , and accuracy of [Formula: see text]. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.

COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images.

Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) images can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. During pandemic era, however, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensive and prone to error, which raises an urgent quest to develop practical autonomous solutions. In this context, first, the paper introduces an open-access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist. Second, a Deep Neural Network (DNN)-based framework is proposed, referred to as the [Formula: see text], that autonomously segments lung abnormalities associated with COVID-19 from chest CT images. Performance of the proposed [Formula: see text] framework is evaluated through several experiments based on the introduced and external datasets. Third, an unsupervised enhancement approach is introduced that can reduce the gap between the training set and test set and improve the model generalization. The enhanced results show a dice score of 0.8069 and specificity and sensitivity of 0.9969 and 0.8354, respectively. Furthermore, the results indicate that the [Formula: see text] model can efficiently segment COVID-19 lesions in both 2D CT images and whole lung volumes. Results on the external dataset illustrate generalization capabilities of the [Formula: see text] model to CT images obtained from a different scanner.

COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans.

The newly discovered Coronavirus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019. The rapid outbreak of this disease has overwhelmed health care infrastructures and arises the need to allocate medical equipment and resources more efficiently. The early diagnosis of this disease will lead to the rapid separation of COVID-19 and non-COVID cases, which will be helpful for health care authorities to optimize resource allocation plans and early prevention of the disease. In this regard, a growing number of studies are investigating the capability of deep learning for early diagnosis of COVID-19. Computed tomography (CT) scans have shown distinctive features and higher sensitivity compared to other diagnostic tests, in particular the current gold standard, i.e., the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Current deep learning-based algorithms are mainly developed based on Convolutional Neural Networks (CNNs) to identify COVID-19 pneumonia cases. CNNs, however, require extensive data augmentation and large datasets to identify detailed spatial relations between image instances. Furthermore, existing algorithms utilizing CT scans, either extend slice-level predictions to patient-level ones using a simple thresholding mechanism or rely on a sophisticated infection segmentation to identify the disease. In this paper, we propose a two-stage fully automated CT-based framework for identification of COVID-19 positive cases referred to as the "COVID-FACT". COVID-FACT utilizes Capsule Networks, as its main building blocks and is, therefore, capable of capturing spatial information. In particular, to make the proposed COVID-FACT independent from sophisticated segmentations of the area of infection, slices demonstrating infection are detected at the first stage and the second stage is responsible for classifying patients into COVID and non-COVID cases. COVID-FACT detects slices with infection, and identifies positive COVID-19 cases using an in-house CT scan dataset, containing COVID-19, community acquired pneumonia, and normal cases. Based on our experiments, COVID-FACT achieves an accuracy of 90.82 % , a sensitivity of 94.55 % , a specificity of 86.04 % , and an Area Under the Curve (AUC) of 0.98, while depending on far less supervision and annotation, in comparison to its counterparts.

COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning.

Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 2 million lives, since its emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a timely fashion, can rapidly incapacitate healthcare systems. The current standard diagnosis method, the Reverse Transcription Polymerase Chain Reaction (RT- PCR), is time consuming, and subject to low sensitivity. Chest Radiograph (CXR), the first imaging modality to be used, is readily available and gives immediate results. However, it has notoriously lower sensitivity than Computed Tomography (CT), which can be used efficiently to complement other diagnostic methods. This paper introduces a new COVID-19 CT scan dataset, referred to as COVID-CT-MD, consisting of not only COVID-19 cases, but also healthy and participants infected by Community Acquired Pneumonia (CAP). COVID-CT-MD dataset, which is accompanied with lobe-level, slice-level and patient-level labels, has the potential to facilitate the COVID-19 research, in particular COVID-CT-MD can assist in development of advanced Machine Learning (ML) and Deep Neural Network (DNN) based solutions.

Spontaneous pneumothorax and pneumomediastinum as a rare complication of COVID-19 pneumonia: Report of 6 cases.

Spontaneous pneumothorax (SPT) and pneumomediastinum (SPM) have been reported as uncommon complications of coronavirus disease (COVID-19) pneumonia. The exact incidence and risk factors are still unrecognized. We report 6 nonventilated, COVID-19 pneumonia cases with SPT and SPM and their outcomes. The major risk factors for development of SPT and SPM in our patients were male gender, advance age, and pre-existing lung disease. These complications may occur in the absence of mechanical ventilation and associated with increasing morbidity (chest tube insertion, sepsis, hospital admission) and mortality. SPT and SPM should be considered as a potential predictive factor for adverse outcome and probable cause of unexplained deterioration of clinical condition in COVID-19 pneumonia.

COVID-19, myocardial edema and dexamethasone.

Severe acute respiratory syndrome corona virus 2(SARS-CoV-2), the cause of coronavirus disease- 2019 (COVID-19) after emerging in china in late 2019 is spreading rapidly across the world. The most common cause of death in patient with COVID-19 is the rapid progression of acute respiratory distress syndrome (ARDS) shortly after the beginning of dyspnea and hypoxemia. Patients with severe COVID-19 may also develop acute cardiac, kidney and liver injury that are associated with poor prognosis and can lead to high mortality rate. Numerous randomized trials are ongoing to find an effective, safe and widely available treatment. Remdisivir is the only FDA -approved antiviral agent for treatment of severe COVID-19. Glucocorticoids (GCs) have been used for treatment of cytokine storm syndrome and respiratory failure in hospitalized patient with severe covid-19. One of the therapeutic effects of GCs is stability of vascular endothelial barrier and decreasing tissue edema. In our opinion, the decreasing vascular permeability effect of glucocorticoids in the injured myocardium might has an important additional factor in reducing mortality in severe, hospitalized COVID-19 patients.

Takayasu's arteritis associated with Crohn's disease: a case report.

INTRODUCTION: The simultaneous presence of Takayasu's arteritis and Crohn's disease in a patient seems to be rare. To our knowledge, no patient with the combination of Crohn's disease and Takayasu's arteritis has been reported from our region. CASE PRESENTATION: Herein we present the case of a 22-year-old Iranian woman previously diagnosed as Crohn's disease and who had subsequently developed Takayasu's arteritis. CONCLUSION: Clinical suspicion, proper imaging, and consideration of the differential diagnosis are important for the correct diagnosis and management of patients with this coincidence.