Spatial dispersion analysis of LGE-CMR for prediction of ventricular arrhythmias in patients with cardiac sarcoidosis

Advances in the diagnosis of myocarditis in idiopathic inflammatory myopathies: an overview of diagnostic tests

Cardiac involvement in idiopathic inflammatory myopathies (IIM) purports to worse clinical outcomes, and therefore early identification is important. Research has focused on blood biomarkers and basic investigations such as ECG and echocardiography, which have the advantage of wide availability and low cost but are limited in their sensitivity and specificity. Imaging the myocardium to directly look for inflammation and scarring has therefore been explored, with a number of new methods for doing this gaining wider research interest and clinical availability. Cardiovascular magnetic resonance (CMR) with contemporary multiparametric mapping techniques and late gadolinium enhancement imaging, is an extremely valuable and increasingly used non-invasive imaging modality for the diagnosis of myocarditis. The recently updated CMR-based Lake Louise Criteria for the diagnosis of myocarditis incorporate the newer T1 and T2 mapping techniques, which have greatly improved the diagnostic accuracy for IIM myocarditis.18F-FDG-PET/CT is a well-utilized imaging modality in the diagnosis of malignancies in IIM, and it also has a role for the diagnosis of myocarditis in multiple systemic inflammatory diseases. Endomyocardial biopsy, however, remains the gold standard technique for the diagnosis of myocarditis and is necessary for the diagnosis of specific cases of myocarditis. This article provides an overview of the important tests and imaging modalities that clinicians should consider when faced with an IIM patient with potential myocarditis.

The beating heart: artificial intelligence for cardiovascular application in the clinic

Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.

Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death

Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.