A comparative study of functional impairment across clinical subtypes of MINOCA: insights from feature tracking cardiac MRI

Abstract Background Myocardial infarction with non-obstructive coronary arteries (MINOCA) encompasses a heterogeneous spectrum of underlying pathophysiological mechanisms. While cardiac MRI (CMR) plays a central role in identifying the etiology, the diagnostic value of myocardial strain analysis using feature tracking (FT-CMR) remains underexplored. Purpose This study aims to investigate the final diagnostic subtypes revealed after the initial diagnosis of MINOCA using CMR, and to assess myocardial functional impairment across these subtypes using FT-CMR-derived strain parameters. We further explore whether strain patterns can aid in etiological differentiation. Methods We retrospectively analyzed CMR data from 216 MINOCA patients (mean age 62 ± 16 years; 67.6% female) who underwent imaging within 5 days of presentation. Patients were categorized into diagnostic subgroups based on CMR tissue characterization findings: Takotsubo syndrome, myocarditis, vessel-related MINOCA (true MINOCA), and other/indeterminate causes. Global longitudinal strain (GLS), global circumferential strain (GCS), and global radial strain (GRS) were quantified using feature tracking. Intergroup comparisons were performed using the Mann–Whitney U test and Kruskal–Wallis test. Results Significant differences in strain parameters were observed across the subtypes. Takotsubo patients showed the most pronounced reduction in left ventricular ejection fraction (EF, p = 0.001), GLS (p < 0.001), and GCS (p < 0.001 ) using the Kruskal–Wallis test. In contrast, myocarditis was associated with milder impairment of ventricular function. These findings demonstrate that different MINOCA etiologies are associated with distinct degrees of myocardial dysfunction. Additionally, we observed distinct patterns of risk factors associated with each subtype, with age and hypertension showing statistically significant differences between the study groups Conclusion FT-CMR reveals distinct functional patterns across clinical subtypes of MINOCA. Strain analysis has potential as a complementary diagnostic tool, enhancing etiological differentiation and supporting the development of machine learning algorithms to improve diagnostic accuracy and guide clinical management in MINOCA