Society for Cardiovascular Magnetic Resonance reference values ("normal values") in cardiovascular magnetic resonance: 2025 update

Risk Stratification of Left Ventricle Hypertrabeculation Versus Non-Compaction Cardiomyopathy Using Echocardiography, Magnetic Resonance Imaging, and Cardiac Computed Tomography

Non-compaction cardiomyopathy (NCCM) is a rare, congenital form of cardiomyopathy characterized by excessive trabeculations in the left ventricle myocardium. NCCM is often an underdiagnosed heart condition characterized by abnormal myocardial trabeculations, which can lead to a wide range of clinical outcomes, from asymptomatic cases to severe heart failure and arrhythmias. It is often diagnosed using imaging techniques like echocardiography, cardiac magnetic resonance imaging (CMR), and cardiac computed tomography (CT), which help assess the heart's structure and function. Although echocardiography remains a key tool, CMR is a gold standard for left ventricle NCCM diagnosis, structural and functional assessment, accurate trabecular quantification, detects fibrosis with late gadolinium enhancement, superior in risk stratification for sudden cardiac death. CT has gained importance in diagnosing NCCM, especially in ruling out coronary artery disease and evaluating complications such as left ventricle thrombus. Accurate risk stratification is crucial for identifying high-risk patients and providing timely interventions. This review examined the contributions of echocardiography, CMR, and cardiac CT in diagnosing NCCM, assessing disease severity, and guiding treatment decisions. We highlighted the strengths and limitations of each imaging modality, discussing their ability to detect myocardial abnormalities, evaluate heart function, and identify fibrosis or other structural changes. We also emphasized integrating imaging findings with clinical and genetic data to enhance patient management and outcomes. Finally, we explored the potential future applications of artificial intelligence in improving diagnostic accuracy and refining risk assessment in NCCM.

Cardiac Magnetic Resonance Guidance for the Pathogenetic Definition of Cardiomyopathies

PURPOSE OF REVIEW

Pathogenetics is the study of genetics in disease pathogenesis. Many abnormal gene alleles have been identified in cardiomyopathies, but their clinical utility remains limited. This review aims to examine the integration of cardiac MRI (CMR) with genetic data to enhance early detection, prognostication, and treatment strategies for cardiomyopathies.

RECENT FINDINGS

CMR is the gold standard imaging modality for cardiomyopathy evaluation, capable of detecting subtle structural and functional changes throughout the disease course. When applied to patients with genetic mutations, with or without phenotypic expression, CMR aids in early diagnosis and risk stratification. Cardiomyopathies can be categorized into at least seven clinical groups based on morphology, function, and genetic associations: (1) Dilated cardiomyopathy (DCM), (2) Hypertrophic cardiomyopathy (HCM), (3) Restrictive cardiomyopathy, including transthyretin amyloidosis (ATTR-CM), iron overload, and Anderson-Fabry disease, (4) Arrhythmogenic cardiomyopathy (ACM), (5) Non-dilated left ventricular cardiomyopathy (NDLVC), (6) Peripartum cardiomyopathy, and (7) Muscular dystrophy-related cardiomyopathy. We have described left ventricular noncompaction (LVNC) as a morphological trait rather than a distinct cardiomyopathy. Emerging CMR and genetic data suggest an inflammatory component in DCM and ACM, with potential therapeutic implications for immunotherapy. Advanced CMR techniques, such as quantitative perfusion, can distinguish cardiomyopathies from ischemic heart disease and detect early microvascular dysfunction, particularly in ATTR-CM and HCM. Late gadolinium enhancement (LGE) and parametric mapping (T1 and extracellular volume [ECV]) further enhance early diagnosis, prognostication and treatment response by assessing fibrosis and myocardial composition. The integration of CMR and genetic insights improves our understanding of cardiomyopathy pathogenesis, aiding in early diagnosis and prognostic assessment. Future research should leverage artificial intelligence (AI) to analyze genetic and radiomic CMR features, including perfusion data, to establish a comprehensive pathogenetic framework. This approach could refine disease classification, identify novel therapeutic targets, and advance precision medicine in cardiomyopathy management.