Inter-study reproducibility of cardiovascular magnetic resonance tagging

MESA was a prospective, population-based, epidemiological study to investigate the prevalence and progression of subclinical cardiovascular disease in a multi-ethnic cohort (Caucasian, African American, Hispanic, and Chinese) of men and women 45 to 84 years of age. The characteristics of MESA subjects have been described previously [16]. CMR tagging was performed in a cohort of 1,030 from 6 different sites, out of which 330 participants were from Baltimore, Maryland. Of these 330 participants, 25 participants were available and consented to a repeat CMR tagging examination. The local ethics board committee approved the study. In this ancillary study, after obtaining informed consent, these 25 participants had CMR tagging performed twice over 12 ± 7 days (range, 7–28 days), 7 females (28%), 18 males (72%), mean age 66 ± 7.1 years, range 53–80 years, Caucasians 64%, and African Americans 36%.

Myocardial CMR-tagged images were obtained with 1.5 T MR Systems (Avanto, Siemens Medical Solutions, Germany). Images were acquired using segmented k-space; electrocardiogram-gated fast low-angle shot (FLASH) pulse sequence. The average scan time was about three minutes. The parameters for tagged images included the following: field of view 360 × 360 mm, slice thickness 10 mm, slice gap 10 mm, Echo time 2.5 ms, flip angle 10°, matrix size 256 × 128, phase-encoding views per segment 4 to 9, spatial resolution 1.4 × 2.8 × 10, temporal resolution 35 ms, tag spacing 7 mm.

The technicians were trained on the MESA CMR protocol and appropriate instructions were provided for the tagging sequences. Images were acquired at resting lung volume and the basal slice was chosen 2 cm below the mitral valve. The short axis images were obtained perpendicular to the inter-ventricular septum, with the three slices planned at the systolic phase. To evaluate any physiological variation in strain measurement, participants were advised to return on a different day for a repeat scan, with no restrictions to diet prior to the scan. The purpose of obtaining the study on a different day—within a period of no expected clinical change in the participant—from the initial scan was also to ensure that the strain measurements could be reliably obtained in the LV if the tagging protocol for appropriate short axis plane placement was followed consistently by the technician. This was to approximate to a clinical practice scenario where the sequential scans were performed by different technicians.

To assess the role of slice orientation on LV Ecc measurement, two healthy volunteers were enrolled. After obtaining the initial scan, the image acquisition was repeated with slices rotated ±15 degrees out of the true short axis, followed by a repeat scan in true short axis. The volunteer was then advised to have a second scan in the true short axis plane 15 minutes from completion of the initial scan (Figure 1). To assess the role of slice location on LV Ecc measurement, two healthy volunteers had whole heart tagging with multiple short axis-tagged slices obtained from base to apex covering the entire heart. The parameters for tagged images were the same as described above with a slice gap of 10 mm and a slice thickness of 10 mm.

Tagged short-axis slices were analyzed by HARP (Diagnosoft, Inc., Palo Alto, CA). After importing the images, the short-axis images with horizontal and vertical tags were superimposed. The band-pass filter was selected automatically by HARP on the spectral peak adjacent to the Fourier space to produce the harmonic images. Endocardial and epicardial contours were manually traced on the image in end-systolic phase in each slice. HARP then automatically segmented the LV myocardium in 24 equal-sized regions each, with three layers: subepicardium, midwall, and subendocardium. This was visualized as a circular grid and tracked along the remaining cardiac phases. A few interactive corrections of the contour tracking were performed as necessary to obtain satisfactory matching. The average analysis time per study was about 10 minutes with a range of 8–12 minutes. To assess intraobserver reproducibility, a single reader performed the myocardial strain analysis twice for all image data sets. To assess interobserver reproducibility, a second reader performed analysis of all the data sets.

Peak midwall systolic strain (peak segmental strain) was determined for 16 segments of the AHA 16-segment model [17] using MATLAB software (The Math Works, MA, USA) [10]. Average midwall strain was calculated for each of the three slices averaging the corresponding peak segmental strain values from the AHA 16-segment model. The strain parameters obtained include Circumferential shortening, Ecc; radial thickening, Err; maximal elongation, Lambda 1; maximal shortening, Lambda 2; angle between the direction of Ecc, and Lambda 2, α (angle from here after). Strain rates were obtained by taking the first derivative of circumferential strain measurements over time for each LV segment. The time to peak systolic circumferential strain (ms), systolic circumferential strain rate (1/s), and early diastolic circumferential strain rate (ratio/s) were measured for the mid-ventricular midwall. Torsion has been defined in different ways in the literature [18]. We used the definition of LV Torsion as the difference in rotation (ϕ) between base and apex divided by the distance (D) between the measured locations of base and apex. Time to peak torsion, systolic torsion rate, and early diastolic torsion rate were determined.

All continuous variables were represented by mean ± standard deviation and Categorical data were presented as percentages. Paired t-test was used to determine the differences in continuous variables. All tests were two-tailed and a p value <0.05 was considered to be statistically significant. The difference in strain variables between two exams was represented by mean difference and standard deviation of mean difference. Reliability was assessed using the intraclass correlation coefficient (ICC) with a model of absolute agreement; absolute measurement error was estimated by the standard error of measurement (SEM) and smallest detectable change (SDC) [19]. The SEM—defined as SEM = SD x √ (1-ICC) where SD = standard deviation of mean difference—takes the amount of measurement error into consideration and quantifies the within-subject variability. SDC—calculated as SDC = 1.96 x SEM x √2, where 1.96 corresponds to 95% confidence interval and the square root of 2 is to adjust for sampling from two different measurements—represents the 95% confidence that a change in the measurement exceeding this threshold is true and reliable and not just a measurement error. Bland-Altman analysis and Passing-Bablok regression [20] was performed to visualize the agreement and measurement error between the repeated studies. The degree of agreement between the two studies was determined by the mean difference and 95% confidence intervals of mean difference [21]. Statistical analysis was performed using SPSS statistical software version 19 (SPSS Inc., Chicago). Bland-Altman analysis and Passing-Bablok regression was performed using MedCalc, version 10.2.0.0 (MedCalc Software, Mariakerke, Belgium).

The mean age of these 25 participants was 66.4 ± 7.15 years (18 men and 7 women). Of these, 28% had diabetes mellitus, 56% were hypertensive, 64% were current smokers, and 16% had hyperlipidemia. There was no significant difference between the heart rates at both exams (61.6 ± 14.8 at exam 1, 62.7 ± 16.3 at exam 2 with a p value of 0.81). Systolic and diastolic blood pressure (SBP, DBP) was similar in both exams (SBP = 122.3 ± 18.1 and 119.5 ± 14.3 with a p value of 0.4; DBP = 72.5 ± 9.8 and 71.8 ± 8.7 with a p value of 0.6 at exam 1 and exam 2 respectively). Image quality was good for analysis in all the subjects. Multivariable linear regression demonstrated that traditional risk factors (age, gender, ethnicity, heart rate, and systolic and diastolic blood pressure) had no significant association on the reproducibility of strain measurements.

Intra- and interobserver reproducibility of strain and torsion parameters is demonstrated in Table 1. Intra- and interobserver reproducibility was excellent for average midwall peak Ecc and Lambda 2, LV torsion and peak diastolic torsion rate (ICC range 0.7 to 0.9). Err, Lambda 1 and angle had excellent intra- and interobserver reproducibility in the basal and mid slices compared to apical slices. Time to peak Ecc had moderate intraobserver reproducibility (ICC = 0.5) compared to interobserver reproducibility (ICC = 0.3). Time to peak torsion had excellent intraobserver reproducibility (ICC = 0.9) than interobserver agreement (ICC = 0.5), while peak systolic torsion rate had moderate interobserver reproducibility (ICC = 0.6) compared to intraobserver reproducibility (ICC = 0.4). Circumferential systolic and early diastolic strain rates were less reproducible compared to Ecc.

Several studies have been conducted in the MESA study using similar imaging protocol for CMR tagging; LV strain and torsion parameters were measured using HARP analysis. Previous studies results have demonstrated a significant association between LV strain and markers of atherosclerosis and subclinical cardiovascular disease [11‐15]. Age-related changes in LV strain and torsion have been associated with ventricular and vascular remodeling which conferred a higher hazard of total cardiovascular events [28, 29]. The excellent inter-study reproducibility of Ecc and LV torsion in the current study can thus aid in both diagnosis and follow-up of subclinical and clinical cardiovascular disease. In addition, the current study included both men and women, with a mean age of 66 years, who had underlying traditional cardiovascular risk factors, which is similar to a clinical setting. The image quality was adequate in all the participants; thus, the results from this study may be clinically applicable to the general population.

Our study used two-dimensional strain calculations to derive strain parameters; thus, the effect of through-plane motion was not determined. Several new techniques have been proposed for three-dimensional strain analysis including 3D complimentary SPAMM imaging [30], 3D HARP technique [31], zHARP [32], and phase unwrapping in HARP [33]. Larger studies using these newer techniques are needed to understand the regional myocardial function in different cardiovascular diseases. Another major limitation of this study is the image acquisition of only short-axis images and the lack of long-axis images, thus a lack of longitudinal strain measurement. Further studies are needed to determine the effect of scanner, magnetic field strength, and tagging pulse sequence on the reproducibility of strain measurement.