JCM, Vol. 14, Pages 6198: Standardized Myocardial T1 and T2 Relaxation Times: Defining Age- and Comorbidity-Adjusted Reference Values for Improved CMR-Based Tissue Characterization

Journal of Clinical Medicine doi: 10.3390/jcm14176198

Authors:
Mukaram Rana
Vitali Koch
Simon Martin
Thomas Vogl
Marco M. Ochs
David M. Leistner
Sebastian M. Haberkorn

Background: This study aims to establish standardized reference values for myocardial T1 and T2 relaxation times in a clinically and imaging-defined real-world patient cohort, evaluating their variability in relation to age, sex, and comorbidities. By identifying key physiological and pathological influences, this investigation seeks to enhance CMR-based myocardial mapping for improved differentiation between normal and pathological myocardial conditions. Methods: This retrospective observational study analyzed T1 and T2 relaxation times using CMR at 1.5 Tesla in a cohort of 491 subjects. T1 and T2 times were measured using MOLLI and GRASE sequences, and statistical analyses assessed intra- and interindividual variations, including the influence of age, sex, and comorbidities, to establish reference values and improve myocardial tissue characterization. Results: T1 and T2 relaxation times were analyzed in 291 and 200 participants, respectively. The mean global T1 time was 1004.7 ± 49.8 ms, with no significant differences between age groups (p = 0.81) or sexes (p = 0.58). However, atrial fibrillation (AF) and mitral regurgitation (MR) were associated with significantly prolonged T1 times (p < 0.05). The mean global T2 time was 67.4 ± 8.6 ms, with age-related prolongation (p < 0.05), but no sex differences (p = 0.46). Comorbidities did not significantly influence T2 times, except for NYHA Class III–IV patients, who exhibited prolonged T2 values (p < 0.05). Conclusions: Standardized T1 and T2 reference values are essential to improve diagnostic accuracy and risk stratification in CMR-based myocardial tissue characterization. Future research should focus on multicenter validation, AI-driven analysis, and the development of age- and comorbidity-adjusted normative databases to enhance individualized cardiovascular care.

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Mukaram Rana www.mdpi.com