This is a retrospective, single-center, cross-sectional study involving the recruitment of 208 volunteers via a public call spanning from September 2017 to December 2020. Ethical approval was obtained from the local ethics committee (Ethikkommission der Medizinischen Fakultät der Ruhr-Universität Bochum, Sitz Ostwestfalen, registration number: 2017–238, with an amendment registration number 2022–924), adhering to the principles outlined in the Declaration of Helsinki's seventh revision of 2013. Prior to participation, all volunteers or their legal guardians provided informed consent.

Participants underwent a comprehensive health questionnaire to select only healthy individuals, devoid of cardiovascular diseases, personal or familial cardiac history, and associated risk factors, were included in the study. Demographic information including age, gender, weight, and height was collected from the questionnaire. Additionally, it was ensured that no contra-indications for CMR assessment were present. Routine biventricular CMR assessments, encompassing left and right ventricular function parameters such as ejection fractions and left ventricular muscle mass, were conducted to validate data consistency against established norms [5].

Eighteen participants were excluded due to health criteria violations, while technical constraints or inadequate image quality led to the exclusion of fifteen additional participants (Fig. 1).

Flow chart to identify healthy study participants suitable for performing right-ventricular strain analysis

All participants underwent cardiac MRI imaging utilizing a 3.0 Tesla multi-transmit magnetic resonance imaging system (Achieva, Philips Healthcare, Best, The Netherlands; Release 5.3.1 and 5.6.1), which incorporates advanced dStream technology. Cardiac cine acquisitions were performed using vector electrocardiogram triggering, ensuring precise synchronization. The system boasted a maximum gradient performance of 40 mT/m with a slew rate of 200 mT/m/ms, while signal reception was facilitated by cardiac phased-array coils.

Our imaging protocol comprised an axially acquired stack covering the entire heart, alongside a short-axis stack encompassing both left and right ventricles (typically 12–16 slices, with no gaps) as well as 2-chamber, 3-chamber and 4-chamber views according to the guidelines, employing cine steady-state free-precession acquisitions (TR/TE/flip angle = 2.7 ms/1.35 ms/42°). Volumetric and RV strain assessment were enabled utilizing both 4-chamber long-axis and short-axis views. With a rapid acquisition rate, 45 reconstructed heart frames (interpolated, 32 acquired cardiac phases) were captured within a single cardiac cycle, ensuring a temporal resolution of < 30 ms. Spatial resolution was optimized at 1.5 × 1.5 × 8 mm3, facilitating precise anatomical delineation and accurate assessment of cardiac function. All examinations were conducted by a single investigator in order to minimize subjective interactions.

Strain analysis was conducted using the CVI42® software package (Circle Cardiovascular Imaging Inc., Calgary, Canada, Release 5.12.1) based on cine steady-state free-precession acquisitions. The 4-chamber and short axis views were utilized for right ventricular strain assessment. The RV strain assessment was carried out by two experienced evaluators with > 3 years of experience.

Briefly, after loading the patient data, the Dubois formula was selected for the calculation of the body-surface-indexed ventricular volumes. Before starting the strain analysis, the “Use simplified endocardial contours” option under the preferences tab, subfolder Contours, and the “Apply a temporal smoothing” option under the subfolder Strain, were activated. Out of the typically acquired 3–5 four-chamber slices, the most suitable 4-chamber slice, showing the largest RV-extension, was selected for RV strain analysis.

To perform the RV strain analysis, both the short axis (SA) stack and the previously selected, most suitable 4-chamber slice were loaded into the CVI42® strain module (Fig. 2). Subsequently, the appropriate end-systolic and end-diastolic heart frames were determined. Using the option “Detect SAX contours current phase” for the SA stack and the option “Contours current 4 CV image” for the relevant 4-chamber slice, the CVI42® software automatically drew contours for these two heart frames. To improve the reliability of the RV strain analysis, the basal SA slices representing the right ventricular outflow track and the apical SA slices with no obvious RV blood volume in end-systole were excluded from RV strain analysis. If needed, manual correction of the endocardial SA contours and the endocardial 4-chamber contours was made. The epicardial contours for both the SA stack and the selected 4-chamber slice had to be precisely drawn manually. RV strain analysis was initiated based on an end-diastolic heart frame. If required, the end-systolic and end-diastolic heart frames must be readjusted. This is because the software can only calculate the end-systolic and end-diastolic volumes based on the remaining slices due to the deletion of some SA slices in the previously described step, resulting in a wrong definition of the heart frames for end-systole and end-diastole.

Illustration of slice-selection in the 4-chamber view at end-diastole (red dashed line) in order to capture the maximum dimension of right ventricular (RV) wall deformation. (a) 4-Chamber view (b) short axis view. Yellow lines represent the borders of the right endocardium, blue lines correspond to the borders of the right epicardium. (c, d) Boundary points to visualize displacement, direction and vector length for calculating of the cardiac strain. (e) Time-to-strain curves displaying the RV global longitudinal strain (orange), RV global circumferential strain (yellow), and RV global radial strain (green)

The RV strain results for global longitudinal strain, global circumferential strain, and global radial strain should then be carefully checked. This can be done by displaying the strain results in cine mode by selecting the CVI42® options “Boundary points” and/or “Mesh”. Finally, the strain data (“scientific report”) is saved for reporting and export as a text file for later extraction of the relevant data (strain and strain rate values, time-to-peak data, displacement data etc.) into the database.

Alongside volumetric data, peak systolic and diastolic longitudinal, circumferential and radial strain and strain rate was quantified.

Statistical analysis was conducted using SPSS (version 29.0, IBM Deutschland GmbH). Normal distribution of data was assessed using the Shapiro–Wilk test. Continuous variables were reported as mean ± standard deviation (SD) for normally distributed data, while non-normally distributed data were presented as median with interquartile range. Gender differences were evaluated using the Mann–Whitney U-test for non-normally distributed data and the unpaired Student’s t-test for normally distributed data. Correlations between right ventricular strain and strain rate with age was assessed using either the Spearman or Pearson correlation coefficient, depending on the fulfillment of necessary assumptions. Prior to conducting Pearson or Spearman’s Rho correlation analysis, linearity, normal distribution, and the presence of outliers were carefully evaluated. The relationship between > 2 predictors and a dependent variable was determined by multilinear regression analysis. The analysis was only accepted if the prerequisites for conducting a multilinear regression analysis, including linearity, checking for outliers, independence of residuals, multicollinearity, homoscedasticity and normal distribution according to Hemmerich, were met [19]. The p-value < 0.05 was considered statistically significant. The correlation coefficient (r resp. rho) was interpreted according to Cohen's guidelines [20]. A strong correlation was defined for r resp. rho values above 0.5, a moderate correlation for values between 0.3 and 0.5, and a weak correlation for values between 0.1 and 0.3. Moreover, right ventricular strains were assessed against age-segments, divided in groups of subjects below 30 years, subjects between 30 – 50 years and subjects above 50 years of age. The intra- and interrater reliability was examined utilizing both the intraclass correlation coefficient (ICC), coefficient of variation and Bland–Altman statistics.