T2 and T2⁎ mapping and weighted imaging in cardiac MRI

Cardiac imaging is progressing from simple imaging of heart structure and function to techniques visualizing and measuring underlying tissue biological changes that can potentially define disease and therapeutic options. These techniques exploit underlying tissue magnetic relaxation times: T1, T2 and T2*. Initial weighting methods showed myocardial heterogeneity, detecting regional disease. Current methods are now fully quantitative generating intuitive color maps that do not only expose regionality, but also diffuse changes – meaning that between-scan comparisons can be made to define disease (compared to normal) and to monitor interval change (compared to old scans). T1 is now familiar and used clinically in multiple scenarios, yet some technical challenges remain. T2 is elevated with increased tissue water – edema. Should there also be blood troponin elevation, this edema likely reflects inflammation, a key biological process. T2* falls in the presence of magnetic/paramagnetic materials – practically, this means it measures tissue iron, either after myocardial hemorrhage or in myocardial iron overload. This review discusses how T2 and T2⁎ imaging work (underlying physics, innovations, dependencies, performance), current and emerging use cases, quality assurance processes for global delivery and future research directions

The fractal heart — embracing mathematics in the cardiology clinic

For clinicians grappling with quantifying the complex spatial and temporal patterns of cardiac structure and function (such as myocardial trabeculae, coronary microvascular anatomy, tissue perfusion, myocyte histology, electrical conduction, heart rate, and blood-pressure variability), fractal analysis is a powerful, but still underused, mathematical tool. In this Perspectives article, we explain some fundamental principles of fractal geometry and place it in a familiar medical setting. We summarize studies in the cardiovascular sciences in which fractal methods have successfully been used to investigate disease mechanisms, and suggest potential future clinical roles in cardiac imaging and time series measurements. We believe that clinical researchers can deploy innovative fractal solutions to common cardiac problems that might ultimately translate into advancements for patient care

Myocardial Fibrosis in Heart Failure: Anti-Fibrotic Therapies and the Role of Cardiovascular Magnetic Resonance in Drug Trials

All heart muscle diseases that cause chronic heart failure finally converge into one dreaded pathological process that is myocardial fibrosis. Myocardial fibrosis predicts major adverse cardiovascular events and death, yet we are still missing the targeted therapies capable of halting and/or reversing its progression. Fundamentally it is a problem of disproportionate extracellular collagen accumulation that is part of normal myocardial ageing and accentuated in certain disease states. In this article we discuss the role of cardiovascular magnetic resonance (CMR) imaging biomarkers to track fibrosis and collate results from the most promising animal and human trials of anti-fibrotic therapies to date. We underscore the ever-growing role of CMR in determining the efficacy of such drugs and encourage future trialists to turn to CMR when designing their surrogate study endpoints

Why democratize bioinformatics?

Network bioinformatics and web-based data collection instruments have the capacity to improve the efficiency of the UK’s appropriately high levels of investment into cardiovascular research. A very large proportion of scientific data falls into the long-tail of the cardiovascular research distribution curve, with numerous small independent research efforts yielding a rich variety of specialty data sets. The merging of such myriad datasets and the eradication of data silos, plus linkage with outcomes could be greatly facilitated through the provision of a national set of standardised data collection instruments—a shared-cardioinformatics library of tools designed by and for clinical academics active in the long-tail of biomedical research. Across the cardiovascular research domain, like the rest of medicine, the national aggregation and democratization of diverse long-tail data is the best way to convert numerous small but expensive cohort data sources into big data, expanding our knowledge-base, breaking down translational barriers, improving research efficiency and with time, improving patient outcomes

Cardiovascular Magnetic Resonance Imaging in Familial Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) is a common cause of non-ischaemic heart failure, conferring high morbidity and mortality, including sudden cardiac death due to systolic dysfunction or arrhythmic sudden death. Within the DCM cohort exists a group of patients with familial disease. In this article we review the pathophysiology and cardiac imaging findings of familial DCM, with specific attention to known disease subtypes. The role of advanced cardiac imaging cardiovascular magnetic resonance is still accumulating, and there remains much to be elucidated. We discuss its potential clinical roles as currently known, with respect to diagnostic utility and risk stratification. Advances in such risk stratification may help target pharmacological and device therapies to those at highest risk

Subclinical Hypertrophic Cardiomyopathy in Elite Athletes: Knowledge Gaps Persist

Subclinical hypertrophic cardiomyopathy (HCM) is a phenotypic entity that has emerged from the increased use of cardiovascular magnetic resonance imaging in the evaluation and family screening of patients with HCM. We describe the case of a competitive athlete with a sarcomere gene mutation and family history of HCM who was found to exhibit the subclinical HCM phenotype on cardiovascular magnetic resonance imaging in the absence of left ventricular hypertrophy. We discuss the clinical uncertainties in her management. (Level of Difficulty: Advanced.

Childhood Bradycardia Associates With Atrioventricular Conduction Defects in Older Age: A Longitudinal Birth Cohort Study

Background: This study explored the association between childhood bradycardia and later‐life cardiac phenotype using longitudinal data from the 1946 National Survey of Health and Development (NSHD) birth cohort. // Methods and Results: Resting heart rate was recorded at 6 and 7 years of age to provide the bradycardia exposure defined as a childhood resting heart rate <75 bpm. Three outcomes were studied: (1) echocardiographic data at 60 to 64 years of age, consisting of ejection fraction, left ventricular mass index, myocardial contraction fraction index, and E/e′; (2) electrocardiographic evidence of atrioventricular or ventricular conduction defects by 60 to 64 years of age; and (3) all‐cause and cardiovascular mortality. Generalized linear models or Cox regression models were used, and adjustment was made for relevant demographic and health‐related covariates, and for multiple testing. Mixed generalized linear models and fractional polynomials were used as sensitivity analyses. One in 3 older adults with atrioventricular conduction defects had been bradycardic in childhood, with defects being serious (Mobitz type II second‐degree atrioventricular block or higher) in 12%. In fully adjusted models, childhood bradycardia was associated with 2.91 higher odds of atrioventricular conduction defects (95% CI, 1.59–5.31; P=0.0005). Associations persisted in random coefficients mixed generalized linear models (odds ratio, 2.50; 95% CI, 1.01–4.31). Fractional polynomials confirmed a linear association between the log odds of atrioventricular conduction defects at 60 to 64 years of age and resting heart rate at 7 years of age. There was no association between bradycardia in childhood and mortality outcomes or with echocardiographic parameters and ventricular conduction defects in older age. // Conclusions: Longitudinal birth cohort data indicate that childhood bradycardia trebles the odds of having atrioventricular conduction defects in older age, 88% of which are benign. In addition, it does not influence mortality or heart size and function. Future research should concentrate on identifying children at risk