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To evaluate subclinical cardiac dysfunction in student athletes after COVID-19 infection using feature tracking cardiac MRI strain analysis. Student athletes with history of COVID-19 infection underwent cardiac MRI as part of screening before return to competitive play. Subjects were enrolled if they had no or mild symptoms, normal cardiac MRI findings with no imaging evidence of myocarditis. Feature tracking strain analysis was performed using short and long axis cine MRI images of athletes and a separate cohort of healthy controls. Differences between the cardiac strain parameters were statistically analyzed by Mann-Whitney U test. The study cohort included 122 athletes (49 females, mean age 20 years ± 1.5 standard deviations) who had a history of COVID-19, and 35 healthy controls (24 females, mean age 34 years ± 18 standard deviations). COVID-19 positive athletes had normal physiologic cardiac adaptations, including significantly higher left and right ventricle end-diastolic volumes (p = 0.00001) when compared to healthy controls. There was no significant difference between biventricular ejection fraction between athletes and control subjects (p > 0.05). Cardiac MRI parameters, including left ventricle global longitudinal strain (LV-GLS), global circumferential strain (LV-GCS), and global radial strain (LV-GRS) values were normal but slightly lower in athletes compared to controls. LV-GCS and LV-GRS were significantly lower in athletes compared to controls (p = 0.007 and p = 0.005 respectively), but there was no significant difference for LV-GLS (p = 0.088). In this study of 122 athletes, there was no evidence of subclinical myocardial alterations following recovery from COVID-19 found on cardiac MRI strain analysis. When compared to healthy controls, the competitive athletes had higher end-diastolic volume indices and reduced, albeit normal, strain values of LV-GLS, LV-GCS, and LV-GRS. [ABSTRACT FROM AUTHOR]

The main focus of this work is to introduce a single free-breathing and ungated imaging protocol to jointly estimate cardiac function and myocardial T 1 maps. We reconstruct a time series of images corresponding to k-space data from a free-breathing and ungated inversion recovery gradient echo sequence using a manifold algorithm. We model each image in the time series as a non-linear function of three variables: cardiac and respiratory phases and inversion time. The non-linear function is realized using a convolutional neural networks (CNN) generator, while the CNN parameters, as well as the phase information, are estimated from the measured k-t space data. We use a dense conditional auto-encoder to estimate the cardiac and respiratory phases from the central multi-channel k-space samples acquired at each frame. The latent vectors of the auto-encoder are constrained to be bandlimited functions with appropriate frequency bands, which enables the disentanglement of the latent vectors into cardiac and respiratory phases, even when the data are acquired with intermittent inversion pulses. Once the phases are estimated, we pose the image recovery as the learning of the parameters of the CNN generator from the measured k-t space data. The learned CNN generator is used to generate synthetic data on demand by feeding it with appropriate latent vectors. The proposed approach capitalizes on the synergies between cine MRI and T 1 mapping to reduce the scan time and improve patient comfort. The framework also enables the generation of synthetic breath-held cine movies with different inversion contrasts, which improves the visualization of the myocardium. In addition, the approach also enables the estimation of the T 1 maps with specific phases, which is challenging with breath-held approaches. [ABSTRACT FROM AUTHOR]

Background Eosinophilic myocarditis (EM) secondary to eosinophilic granulomatosis with polyangiitis (EGPA) is a rare disease, for which cardiac magnetic resonance imaging (CMRI) is a useful non-invasive modality for diagnosis. We present a case of EM in a patient who recently recovered from COVID-19 and discuss the role of CMRI and endomyocardial biopsy (EMB) to differentiate between COVID-19–associated myocarditis and EM. Case summary A 20-year-old Hispanic male with a history of sinusitis and asthma, and who recently recovered from COVID-19, presented to the emergency room with pleuritic chest pain, dyspnoea on exertion, and cough. His presentation labs were pertinent for leucocytosis, eosinophilia, elevated troponin, and elevated erythrocyte sedimentation rate and C-reactive protein. The electrocardiogram showed sinus tachycardia. Echocardiogram showed an ejection fraction of 40%. The patient was admitted, and on day 2 of admission, he underwent CMRI which showed findings of EM and mural thrombi. On hospital day 3, the patient underwent right heart catheterization and EMB which confirmed EM. The patient was treated with steroids and mepolizumab. He was discharged on hospital day 7 and continued outpatient heart failure treatment. Discussion This is a unique case of EM and heart failure with reduced ejection fraction as a presentation of EGPA, in a patient who recently recovered from COVID-19. In this case, CMRI and EMB were critical to identify the cause of myocarditis and helped in the optimal management of this patient. [ABSTRACT FROM AUTHOR]

Current deep learning-based manifold learning algorithms such as the variational autoencoder (VAE) require fully sampled data to learn the probability density of real-world datasets. However, fully sampled data is often unavailable in a variety of problems, including the recovery of dynamic and high-resolution magnetic resonance imaging (MRI). We introduce a novel variational approach to learn a manifold from undersampled data. The VAE uses a decoder fed by latent vectors, drawn from a conditional density estimated from the fully sampled images using an encoder. Since fully sampled images are not available in our setting, we approximate the conditional density of the latent vectors by a parametric model whose parameters are estimated from the undersampled measurements using back-propagation. We use the framework for the joint alignment and recovery of multi-slice free breathing and ungated cardiac MRI data from highly undersampled measurements. Experimental results demonstrate the utility of the proposed scheme in dynamic imaging alignment and reconstructions. [ABSTRACT FROM AUTHOR]

Takotsubo cardiomyopathy (TC) is an acute but reversible non-ischemic heart failure syndrome. It is characterized by a transient form of ventricular dysfunction typically manifesting as basal hyperkinesis with hypokinesia and ballooning of left ventricle mid-cavity and apex. Imaging helps in both diagnosis and follow up. Echocardiogram is the first-line modality to assess the typical contractile dysfunction in suspected patients with catheter angiography showing normal coronary arteries. Cardiac MRI is currently the modality of choice for the non-invasive initial assessment of TC and for follow up imaging. The current review focusses on historical background of TC, its pathophysiology, diagnostic work up and differential diagnosis and provides multimodality imaging work up of TC including role of echocardiogram, invasive catheterization, nuclear imaging, cardiac computed tomography and cardiac MRI including basic and advanced MRI sequences. • Takotsubo cardiomyopathy (TC) is reversible syndrome mimicking acute coronary syndrome. • TC is typically characterized by basal hyperkinesis and apical akinesis. • Echocardiography and catheter angiography are often first line modality. • Cardiac MRI is modality of choice for the non-invasive initial assessment. • Cardiac MRI provides comprehensive evaluation for initial diagnosis and follow up. [ABSTRACT FROM AUTHOR]

The role of reliable, non-invasive imaging-based recognition of pulmonary hypertension (PH) remains a diagnostic challenge. The aim of the current pilot radiomics study was to assess the diagnostic performance of cardiac MRI (cMRI)-based texture features to accurately predict PH. The study involved IRB-approved retrospective analysis of cMRIs from 72 patients (42 PH and 30 healthy controls) for the primary analysis. A subgroup analysis was performed including patients from the PH group with left ventricle ejection fraction ≥ 50%. Texture features were generated from mid-left ventricle myocardium using balanced steady-state free precession (bSSFP) cine short-axis imaging. Forty-five different combinations of classifier models and feature selection techniques were evaluated. Model performance was assessed using receiver operating characteristic curves. A multilayer perceptron model fitting using full feature sets was the best classifier model for both the primary analysis (AUC 0.862, accuracy 78%) and the subgroup analysis (AUC 0.918, accuracy 80%). Model performance demonstrated considerable variation between the models (AUC 0.523–0.918) based on the chosen model–feature selection combination. Cardiac MRI-based radiomics recognition of PH using texture features is feasible, even with preserved left ventricular ejection fractions. [ABSTRACT FROM AUTHOR]