Sex-specific structural and functional cardiac remodeling during healthy aging assessed by cardiovascular magnetic resonance.

BACKGROUND: Aging as a major non-modifiable cardiac risk factor challenges future cardiovascular medicine and economic demands, which requires further assessments addressing physiological age-associated cardiac changes. OBJECTIVES: Using cardiovascular magnetic resonance (CMR), this study aims to characterize sex-specific ventricular adaptations during healthy aging. METHODS: The population included healthy volunteers who underwent CMR at 1.5 or 3 Tesla scanners applying cine-imaging with a short-axis coverage of the left (LV) and right (RV) ventricle. The cohort was divided by sex (female and male) and age (subgroups in years): 1 (19-29), 2 (30-39), 3 (40-49), and 4 (≥50). Cardiac adaptations were quantitatively assessed by CMR indices. RESULTS: After the exclusion of missing or poor-quality CMR datasets or diagnosed disease, 140 of 203 volunteers were part of the final analysis. Women generally had smaller ventricular dimensions and LV mass, but higher biventricular systolic function. There was a significant age-associated decrease in ventricular dimensions as well as a significant increase in LV mass-to-volume ratio (LV-MVR, concentricity) in both sexes (LV-MVR in g/ml: age group 1 vs. 4: females 0.50 vs. 0.57, p=0.016, males 0.56 vs. 0.67, p=0.024). LV stroke volume index decreased significantly with age in both sexes, but stronger for men than for women (in ml/m2: age group 1 vs. 4: females 51.76 vs. 41.94, p<0.001, males 55.31 vs. 40.78, p<0.001). Ventricular proportions (RV-to-LV-volume ratio) were constant between the age groups in both sexes. CONCLUSIONS: In both sexes, healthy aging was associated with an increase in concentricity and a decline in ventricular dimensions. Furthermore, relevant age-related sex differences in systolic LV performance were observed.

Comparison of manual and artificial intelligence based quantification of myocardial strain by feature tracking-a cardiovascular MR study in health and disease.

OBJECTIVES: The analysis of myocardial deformation using feature tracking in cardiovascular MR allows for the assessment of global and segmental strain values. The aim of this study was to compare strain values derived from artificial intelligence (AI)-based contours with manually derived strain values in healthy volunteers and patients with cardiac pathologies. MATERIALS AND METHODS: A cohort of 136 subjects (60 healthy volunteers and 76 patients; of those including 46 cases with left ventricular hypertrophy (LVH) of varying etiology and 30 cases with chronic myocardial infarction) was analyzed. Comparisons were based on quantitative strain analysis and on a geometric level by the Dice similarity coefficient (DSC) of the segmentations. Strain quantification was performed in 3 long-axis slices and short-axis (SAX) stack with epi- and endocardial contours in end-diastole. AI contours were checked for plausibility and potential errors in the tracking algorithm. RESULTS: AI-derived strain values overestimated radial strain (+ 1.8 ± 1.7% (mean difference ± standard deviation); p = 0.03) and underestimated circumferential (- 0.8 ± 0.8%; p = 0.02) and longitudinal strain (- 0.1 ± 0.8%; p = 0.54). Pairwise group comparisons revealed no significant differences for global strain. The DSC showed good agreement for healthy volunteers (85.3 ± 10.3% for SAX) and patients (80.8 ± 9.6% for SAX). In 27 cases (27/76; 35.5%), a tracking error was found, predominantly (24/27; 88.9%) in the LVH group and 22 of those (22/27; 81.5%) at the insertion of the papillary muscle in lateral segments. CONCLUSIONS: Strain analysis based on AI-segmented images shows good results in healthy volunteers and in most of the patient groups. Hypertrophied ventricles remain a challenge for contouring and feature tracking. CLINICAL RELEVANCE STATEMENT: AI-based segmentations can help to streamline and standardize strain analysis by feature tracking. KEY POINTS: • Assessment of strain in cardiovascular magnetic resonance by feature tracking can generate global and segmental strain values. • Commercially available artificial intelligence algorithms provide segmentation for strain analysis comparable to manual segmentation. • Hypertrophied ventricles are challenging in regards of strain analysis by feature tracking.