Assessment of Myocardial Iron Overload and Strain Abnormalities in Pediatric β-Thalassemia Using Multiparametric CMR

Background/Objectives: Myocardial iron overload is a major contributor to adverse cardiac outcomes in pediatric patients with transfusion-dependent β-thalassemia (TDT). Cardiovascular magnetic resonance (CMR), including T2* and T1 mapping, allows quantification of myocardial iron and early detection of cardiac dysfunction. Artificial intelligence (AI)-assisted CMR feature tracking (CMR-FT) provides a sensitive and reproducible approach for assessing myocardial deformation, even in patients with preserved left ventricular ejection fraction (LVEF). This study aimed to evaluate the utility of AI-based CMR-FT and its relationship with multiparametric CMR biomarkers, including myocardial strain (GCS, GLS, GRS), tissue characteristics (T2*, T1), and left ventricular (LV) geometry in pediatric TDT patients. Methods: In this retrospective study, 68 pediatric patients with β-thalassemia major and 20 age-matched healthy controls underwent CMR with T2*, T1 mapping, and FT-based strain analysis. Myocardial iron overload was defined as T2* < 20 ms. Strain parameters were compared between groups, and correlations with tissue characteristics and LV geometry were assessed using Pearson's correlation. Results: Patients with myocardial iron overload have significantly reduced GCS compared to controls (-17.4 ± 1.6% vs. -19.3 ± 4.5%, p < 0.01). GCS correlated with T2* (r = -0.33, p = 0.007) and T1 (r = -0.45, p < 0.001). GLS was sensitive to LV geometric changes, particularly concentric remodeling, correlating with LV mass/EDV ratio (r = -0.449, p < 0.001). Conclusions: AI-based CMR-FT combined with multiparametric tissue imaging enhances early detection of subclinical myocardial dysfunction in pediatric TDT, offering diagnostic insights beyond conventional CMR metrics and supporting improved risk stratification.