Myocarditis remains a clinical challenge in pediatrics. Originally, it was recognized at autopsy before the application of endomyocardial biopsy, which led to a histopathology-based diagnosis such as in the Dallas criteria. Given the invasive and low-sensitivity nature of endomyocardial biopsy, its diagnostic focus shifted to a reliance on clinical suspicion. With the advances of cardiac magnetic resonance, an examination of the whole heart in vivo has gained acceptance in the pursuit of a diagnosis of myocarditis. The presentation may vary from minimal symptoms to heart failure, life-threatening arrhythmias, or cardiogenic shock. Outcomes span full resolution to chronic heart failure and the need for heart transplantation with inadequate clues to predict the disease trajectory. The American Heart Association commissioned this writing group to explore the current knowledge and management within the field of pediatric myocarditis. This statement highlights advances in our understanding of the immunopathogenesis, new and shifting dominant pathogeneses, modern laboratory testing, and use of mechanical circulatory support, with a special emphasis on innovations in cardiac magnetic resonance imaging. Despite these strides forward, we struggle without a universally accepted definition of myocarditis, which impedes progress in disease-targeted therapy.
Magnetic resonance imaging (MRI) techniques provide non-invasive and non-ionising methods for the highly accurate anatomical depiction of the heart and vessels throughout the cardiac cycle. In addition, the intrinsic sensitivity of MRI to motion offers the unique ability to acquire spatially registered blood flow simultaneously with the morphological data, within a single measurement. In clinical routine, flow MRI is typically accomplished using methods that resolve two spatial dimensions in individual planes and encode the time-resolved velocity in one principal direction, typically oriented perpendicular to the two-dimensional (2D) section. This review describes recently developed advanced MRI flow techniques, which allow for more comprehensive evaluation of blood flow characteristics, such as real-time flow imaging, 2D multiple-venc phase contrast MRI, four-dimensional (4D) flow MRI, quantification of complex haemodynamic properties, and highly accelerated flow imaging. Emerging techniques and novel applications are explored. In addition, applications of these new techniques for the improved evaluation of cardiovascular (aorta, pulmonary arteries, congenital heart disease, atrial fibrillation, coronary arteries) as well as cerebrovascular disease (intra-cranial arteries and veins) are presented.
Purpose To generate fully automated and fast 4D‐flow MRI‐based 3D segmentations of the aorta using deep learning for reproducible quantification of aortic flow, peak velocity, and dimensions. Methods A total of 1018 subjects with aortic 4D‐flow MRI (528 with bicuspid aortic valve, 376 with tricuspid aortic valve and aortic dilation, 114 healthy controls) comprised the data set. A convolutional neural network was trained to generate 3D aortic segmentations from 4D‐flow data. Manual segmentations served as the ground truth (N = 499 training, N = 101 validation, N = 418 testing). Dice scores, Hausdorff distance, and average symmetrical surface distance were calculated to assess performance. Aortic flow, peak velocity, and lumen dimensions were quantified at the ascending, arch, and descending aorta and compared using Bland‐Altman analysis. Interobserver variability of manual analysis was assessed on a subset of 40. Results Convolutional neural network segmentation required 0.438 ± 0.355 seconds versus 630 ± 254 seconds for manual analysis and demonstrated excellent performance with a median Dice score of 0.951 (0.930‐0.966), Hausdorff distance of 2.80 (2.13‐4.35), and average symmetrical surface distance of 0.176 (0.119‐0.290). Excellent agreement was found for flow, peak velocity, and dimensions with low bias and limits of agreement less than 10% difference versus manual analysis. For aortic volume, limits of agreement were moderate within 16.3%. Interobserver variability (median Dice score: 0.950; Hausdorff distance: 2.45; and average symmetrical surface distance: 0.145) and convolutional neural network–based analysis (median Dice score: 0.953‐0.959; Hausdorff distance: 2.24‐2.91; and average symmetrical surface distance: 0.145‐1.98 to observers) demonstrated similar reproducibility. Conclusions Deep learning enabled fast and automated 3D aortic segmentation from 4D‐flow MRI, demonstrating its potential for efficient clinical workflows. Future studies should investigate its utility for other vasculature and multivendor applications.
BackgroundCardiovascular magnetic resonance (CMR) is increasingly used to diagnose myocarditis in adults but its use in children is not well-established. We sought to describe the presentation, CMR protocol and findings, and outcomes in a multicenter cohort of children with myocarditis.MethodsThirteen hospitals retrospectively identified patients meeting the following inclusion criteria: 1) diagnosis of myocarditis by the managing physicians, 2) age <21 years, 3) CMR examination within 30 days of presentation, and 4) no congenital heart disease. Clinical data and test results, including CMR findings, were abstracted from the medical record.ResultsFor the 143 patients meeting inclusion criteria, the median age was 16.0 years (range, 0.1-20.3) and 139 (97 %) were hospitalized at the time of CMR. The median time from presentation to CMR was 2 days (0-28). The median left ventricular ejection fraction at CMR was 56 % (10-74), with 29 (20 %) below 45 %. The median right ventricular ejection fraction was 54 % (15-72), with 11 (8 %) below 40 %. There was significant variability among centers in the types of tissue characterization techniques employed (p < 0.001). Overall, late gadolinium enhancement (LGE) was used in 100 % of studies, followed by T2-weighted imaging (T2W) in 69 %, first-pass contrast perfusion (FPP) in 48 %, and early gadolinium enhancement (EGE) in 28 %. Abnormalities were most common with LGE (81 %), followed by T2W (74 %), EGE (55 %), and FPP (8 %). The CMR study was interpreted as positive for myocarditis in 117 patients (82 %), negative in 18 (13 %), and equivocal in 7 (5 %), yielding a sensitivity of 82 %. At a median follow-up of 7.1 months (0-87), all patients were alive and 5 had undergone cardiac transplantation. CMR parameters at presentation associated with persistent left ventricular dysfunction were larger left ventricular end-diastolic volume and lower left and right ventricular ejection fraction but not abnormal LGE.ConclusionsDespite significant practice variation in imaging protocol among centers, CMR had a high sensitivity for the diagnosis of myocarditis in pediatric patients. Abnormalities were most often seen with LGE followed by T2W, EGE, and FPP. These findings should be useful in designing future prospective studies.
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