Myocarditis is a significant cause of sudden cardiac death in competitive athletes and can occur with normal ventricular function. 1 Recent studies have raised concerns of myocardial inflammation after recovery from coronavirus disease 2019 (COVID-19), even in asymptomatic or mildly symptomatic patients. 2 Our objective was to investigate the use of cardiac magnetic resonance (CMR) imaging in competitive athletes recovered from COVID-19 to detect myocardial inflammation that would identify high-risk athletes for return to competitive play.Methods | We performed a comprehensive CMR examination including cine, T1 and T2 mapping, extracellular volume fraction, and late gadolinium enhancement (LGE), on a 1.5-T scanner (Magnetom Sola; Siemens Healthineers) using standardized protocols, 3 in all competitive athletes referred to the sports medicine clinic after testing positive for COVID-19 (reverse transcriptase-polymerase chain reaction) between June and August 2020. The Ohio State University institutional review board approved the study, and informed consent in writing was obtained from participating athletes. Cardiac magnetic resonance imaging was performed after recommended quarantine (11-53 days). Electrocardiogram, serum troponin I, and transthoracic echocardiogram were performed on day of CMR imaging.

This cohort study assesses the prevalence of myocarditis in athletes with COVID-19 and compare screening strategies for safe return to play.

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Cardiovascular MRI (CMR) is a non-invasive imaging modality that provides excellent softtissue contrast without the use of ionizing radiation. Physiological motions and limited speed of MRI data acquisition necessitate development of accelerated methods, which typically rely on undersampling. Recovering diagnostic quality CMR images from highly undersampled data has been an active area of research. Recently, several data acquisition and processing methods have been proposed to accelerate CMR. The availability of data to objectively evaluate and compare different reconstruction methods could expedite innovation and promote clinical translation of these methods. In this work, we introduce an open-access dataset, called OCMR, that provides multi-coil k-space data from 53 fully sampled and 212 prospectively undersampled cardiac cine series.

To develop and validate an acquisition and processing technique that enables fully self-gated 4D flow imaging with whole-heart coverage in a fixed 5-minute scan. Theory and Methods: The data are acquired continuously using Cartesian sampling and sorted into respiratory and cardiac bins using the self-gating signal. The reconstruction is performed using a recently proposed Bayesian method called ReVEAL4D. ReVEAL4D is validated using data from 8 healthy volunteers and 2 patients and compared with compressed sensing technique, L1-SENSE. Results: Healthy subjects-Compared with 2D phase-contrast MRI (2D-PC), flow quantification from ReVEAL4D shows no significant bias. In contrast, the peak velocity and peak flow rate for L1-SENSE are significantly underestimated. Compared with traditional parallel MRI-based 4D flow imaging, ReVEAL4D demonstrates small but significant biases in net flow and peak flow rate, with no significant bias in peak velocity. All 3 indices are significantly and more markedly underestimated by L1-SENSE. Patients-Flow quantification from ReVEAL4D agrees well with the 2D-PC reference. In contrast, L1-SENSE markedly underestimated peak velocity. Conclusions: The combination of highly accelerated 5-minute Cartesian acquisition, self-gating, and ReVEAL4D enables whole-heart 4D flow imaging with accurate flow quantification.

Objective: The purpose of the study is to compare phase contrast (PC) imaging with invasive measurements of cardiac output (CO) in patients with pulmonary hypertension (PH). Materials and Methods: We analyzed 81 cases with PH who underwent cardiac magnetic resonance imaging and right heart catheterization (RHC). Measurement of CO and stroke volume (SV) by cardiac magnetic resonance (CMR) was performed by PC imaging of the proximal aorta (Ao) and pulmonary artery (Pa) and by RHC using the Fick and thermodilution (TD) methods. Results: There was good correlation in CO measurements between PC and RHC; however, there was better correlation with SV measurements; Fick-TD (r=0.85), PC-TD (Ao r=0.77, Pa r=0.79), and PC-Fick (Ao r = 0.73, Pa r = 0.78). Bland-Altman analysis of SV showed that Pa PC had slightly lower standard deviation than Ao PC; PC-Fick (Pa SD = 15.11 vs. Ao SD = 16.4 ml) and PC-TD (Pa SD = 16.99 ml vs. Ao SD = 17.4 ml) while Fick-TD had the lowest (SD = 14.4 ml). Compared to Fick, measurement of SV with Ao PC (‒4.12 ml) and Pa PC (0.22 ml) both had lower mean difference than TD (‒11.1 ml). Conclusion: Non-invasive measurement of CO and SV using PC-CMR correlates well with invasive measurement using RHC. Our study showed that PC-CMR had high accuracy and precision when compared to Fick. Among all the modalities, PC-CMR contributed the least amount of variation in measurements.

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