Assessing myocardial strain remains challenging, particularly in the pediatric population, due to the smaller heart sizes, higher heart rates, and variability in strain parameters compared to adult populations. This study aimed to investigate the utility of myocardial strain measurements using cardiac magnetic resonance-feature tracking (CMR-FT) for early diagnosis of Duchenne muscular dystrophy (DMD) in pediatric patients. Twenty-eight DMD patients and 20 healthy controls were involved in this study. Global circumferential, longitudinal, and radial strain (GCS, GLS, and GRS) were measured for the left ventricle (LV) using CMR-FT. Segmental strain values only of the inferolateral and anterolateral LV segments in DMD patients without late gadolinium enhancement (LGE) and DMD patients with LGE were compared to the healthy controls. Strain measurements using CMR-FT in DMD patients were considerably lower than those of healthy controls, with all p-values lower than 0.001. DMD patients without LGE showed decreased inferolateral and anterolateral segmental values only relative to healthy controls. The same behavior was maintained for the LV geometry. Multivariable linear regression demonstrated that the end-systole (ES) wall thicknesses and thickening were associated with decreased GCS and GLS. CMR-FT is crucial in detecting cardiac abnormalities in patients with DMD. It represents an innovative imaging biomarker that can detect initial myocardial alterations in DMD cardiomyopathy without relying on gadolinium.

Computational modeling has contributed to many fields of medicine and has been proven very useful for diagnosing complex diseases. A special case is that of myocardial infarction (MI), a prevalent cardiovascular disease where computational modeling approaches have been useful for detecting abnormalities. The aim of this paper is to propose enhanced computational models for the human left ventricle (LV) to estimate the strain and stress distributions in healthy and diseased subjects. Computational models were developed and evaluated using human LV of 20 patients with MI and 20 healthy controls using cardiac magnetic resonance imaging acquisitions and simulation tools. The finite element technique was employed for LV modeling. Comparative analysis revealed higher global strains in healthy subjects compared to MI patients, particularly global circumferential, longitudinal, and radial strains. The average stress distributions were 67.9 ± 5.01 kPa in healthy models and 78.3 ± 8.21 kPa in infarcted regions. Model-derived strain data indicated an overall average of −0.15 ± 0.06 for healthy models and 0.2 ± 0.04 for infarcted regions. LV strain values were compared with those obtained from two feature-tracking algorithms to validate the proposed models, resulting in very promising findings. The work presented here highlights the importance of computational modeling for quantitative and qualitative analyses of heart disease and the potential of using such models in other organs. When combined with imaging data, the proposed models can have significant implications for improved patient care and treatment strategies. Received: 3 June 2025 | Revised: 30 July 2025 | Accepted: 8 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Rania Awadi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Narjes Benameur: Methodology, Validation, Investigation, Writing – review & editing, Visualization, Supervision, Project administration. Wafa Baccouch: Formal analysis, Writing – review & editing, Visualization. Mohamed Deriche: Methodology, Writing – review & editing, Visualization, Project administration, Funding acquisition. Arnab Palit: Validation, Visualization. Moncef Aloui: Resources, Data curation. Nadjia Kachenoura: Software, Validation, Resources, Data curation, Writing – review & editing, Visualization. Salam Labidi: Methodology, Investigation, Writing – review & editing, Visualization, Supervision, Project administration.

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.