3T MRI investigation of cardiac left ventricular structure and function in a UK population: The tayside screening for the prevention of cardiac events (TASCFORCE) study

Materials and Methods

This MR study was conducted as part of a wider population‐based cardiovascular MR investigation of volunteers asymptomatic of cardiovascular disease (CVD) (the Tayside Screening for the Prevention of Cardiac Events [TASCFORCE] study). The study was allocated an International Standard Randomised Control Trial Number: ISRCTN38976321. Local research ethical committee (REC) approval for the work was obtained and all volunteers gave informed consent. A total of 1528 volunteers were included in the study, which ran from June 2008 until February 2013. Inclusion criteria were as follows: 1) age ≥40 years; 2) free from CVD or other indication for statin therapy as recommended by the Scottish Intercollegiate Guidelines Network (SIGN) report 97 (www.sign.ac.uk) published in February 2007; 3) 10‐year risk of coronary heart disease below 20% as predicted by the Adult Treatment Panel III (ATPIII) algorithm21; and 4) a plasma B type natriuretic peptide (BNP) level greater than the gender specific median. Exclusion criteria included: i) pregnancy; ii) known primary muscle disease; iii) known atherosclerotic disease, including unstable angina, previous myocardial infarction, peripheral arterial disease, amputation, revascularization, hypertension, heart failure, or cerebrovascular event; iv) known diabetes; v) active liver disease; vi) other known illness or contraindication to MR; vii) participation in a clinical trial; viii) inability to give informed consent; ix) known alcohol abuse; and x) blood pressure (BP) of greater than 145/95 mmHg.

Each volunteer was grouped into a “decade band” based on their age at the time of the investigation. The decade bands were defined by age as follows: 1) the “40s” (40–49 years); 2) the “50s” (50–59 years); 3) the “60s” (60–69 years); and 4) the “over 70s” (≥70 years).

The MR protocol has been described in detail elsewhere,22 but in brief imaging was performed in the head‐first supine orientation using a 3T [102x32] Scanner (Magnetom Trio, Siemens, Erlangen, Germany). A body matrix radiofrequency (RF) coil (six elements) was used in combination with a spine array (up to 24 elements).

Three plane localizer steady‐state gradient echo images of the heart were initially obtained using a true fast imaging with steady‐state free precession (TrueFISP) sequence, and these were followed by the acquisition of further localizers in the cardiac two‐chamber (2ch), four‐chamber (4ch), and short axis (SA) orientations. Subsequently, electrocardiogram (ECG)‐gated segmented breath‐hold cinematic (CINE) TrueFISP images were acquired in the LV 4ch and 2ch orientations, and a stack of SA images were acquired from the atrio‐ventricular ring to the LV apex using 2D ECG‐gated breath‐hold segmented CINE TrueFISP sequence with retrospective gating. The sequence parameters for the short‐axis acquisitions were: TR/TE = 3.4/1.5 msec, flip angle >50°, field of view >360 mm (volunteer dependent), pixel matrix 173 × 256, slice thickness 6 mm and interslice gap 4 mm (slices acquired every 10 mm). Retrospective ECG gating was used, with 25 cardiac phases reconstructed (25 lines per segment) and two image slices acquired per breath‐hold. Parallel imaging was also implemented (integrated parallel acquisition technique, iPAT x2), resulting in a scan time of <15 seconds per breath‐hold.

Results

A total of 1515 volunteers were scanned successfully, and a description of demographic information related to anatomic size is shown in Table 1. A further n = 13 volunteers were also scanned (original study size n = 1528) but were excluded from the analysis as a result of either radiographic error or significant movement artifacts experienced during the MR scanning process.

A summary of the results is shown in Table 2. When the normalized data from all female and male participants were examined together the mean EF was higher for females (P < 0.05), but all other variables (EDV, ESV, SV, and LVM) were higher for males (all P < 0.05).

When the LV variables were subdivided into age decade categories (40s, 50s, 60s, and ≥70s) a number of age‐related changes were evident. For males, the mean EF rose slightly over the first decade studied (+1%, P < 0.04) but then remained stable across the remaining age decades. However, the means of all other variables were reduced with age (Fig. 1) (40s vs. ≥70s; EDVI and SVI P < 0.05).

For females, the mean EF rose marginally with age (a 4.0% increase was noted in the ≥70 years cohort relative to the 40–49 years cohort) but the means of all other volumetric variables were noted to decrease with age (40s vs. ≥70s; EDVI, ESVI, SVI P < 0.05). The most stable of all the variables was found to be the mean LVMI, which was only reduced by 1 g/m² in the ≥70 years cohort relative to the 40s cohort. This pattern appeared different from that observed in the male cohort, which demonstrated a more defined reduction in mean LVMI with age.

The results of regression analysis describing the linear change of each LV variable per decade of age are highlighted in Table 3. All variables except for EF were noted to decrease with age, and the EDVI showed the biggest “per decade” reduction (–2.9 ± 1.3 ml/m² for males and –3.1 ± 0.8 ml/m² for females). The biggest difference between the male and female population was noted for LVMI, where greater “per decade” losses were measured in the male cohort (–1.6 ± 1.1 g/m²) relative to the female cohort, which remained virtually stable (–0.2 ± 0.6 g/m²).

A total of n = 782 volunteers satisfied the more stringent low‐risk factor inclusion criteria for the subset cohort, and the resulting data are presented in Appendices 1 (demographics) and 2 (MR parameters). There were no statistically significant differences between the means of any of the normalized LV variables when the data from the full cohort and the subset cohort were compared (P > 0.05 for all LV variables, including all comparisons when subdivided by gender and age).

The variation in normalized mean LV measurements between the different segmentation observers is detailed in Table 4, and illustrated graphically in Fig. 2 for LVM (the most variable measurement). Although the group of volunteers segmented by each observer was different in each case, the data were stratified by age and gender, and normalized to BSA in order to make comparisons closely related to the segmentation technique itself. The consistency of the data between observers for mean EF data ranged from 66 ± 6% (observer 5) to 71 ± 5% (observer 1) for males, and from 68 ± 7% (observer 4) to 73 ± 5% (observer 1) for females. These were similar to the mean EF for the full cohort (Table 2) of 69 ± 6%. For the LV mass index, the consistency of the data ranged from 61 ± 9 g/m² (observer 4), to 69 ± 11 g/m² (observer 2) for males, and from 45 ± 6 g/m² (observer 4) to 55 ± 7 g/m² (observer 2) for females. These also compare favorably to the mean LV mass index for the entire cohort (Table 2) of 55 ± 12 g/m².

Finally, a comparison of the 3.0T data with 1.5T data from elsewhere is presented in table 5. The mean EF was found to be marginally greater at 3.0T relative to 1.5T, but for all other variables the means were a little lower at 3.0T.

Discussion

In this study we present data describing MR LV structure and function in a large cohort of volunteers. The study methodology is similar to others performed previously.1, 2, 3 This work was prepared in response to the specific need for 3.0T MR data of this type as recently reported by Kawel‐Boehm et al.11 Statistical limitations associated with small study cohorts have been addressed by extending this work to include a large asymptomatic population, with full coverage across the adult age range to account for remodeling processes associated with the heart that occur with age.

In this study the means and ranges obtained for LV structure and function parameters at 3.0T are generally similar to those reported at 1.5T. The mean EF was marginally greater at 3.0T relative to 1.5T, but for all other variables the means were a little lower at 3.0T. The reason for these differences is not clear but may be related to variations in edge boundary perception due to changes in the T ₁ (and T ₂) relaxation times of the blood pool, myocardium, and other surrounding tissue structures. There may also be small differences in our study cohort demographics in relation to those reported from elsewhere. The greatest differences were noted for female volunteers, where the mean EDV and LVM parameters were 12 ml and 11 g lower, respectively, at 3.0T when compared to the previous pooled 1.5T data reported by Kawel‐Boehm et al.11 If the individual articles that contribute to this published range are scrutinized more closely, the best agreement to our 3.0T mean EDV (120 ml) is found in the work by Maciera et al,3 who obtained a mean EDV of 126 ml for their cohort of female volunteers in the 50–59 years age decade at 1.5T. Similarly, the closest agreement to our mean LVM (87 g) is reported in the work by Alfakih et al,1 who obtained a mean LVM of 88.1 g for their cohort of female volunteers in the 40–65 years age range at 1.5T. In other words, although some discrepancy exists between the pooled 1.5T data, there are individual contributions that agree quite closely with our findings at 3.0T.

When our 3.0T data were subdivided into male and female groups, the mean data for females were significantly lower than the means for males in all parameters studied, except for EF. This is expected and consistent with findings reported elsewhere.2 Alternative subdivision of the data into age decades (40s, 50s, 60s, and ≥70s) revealed that mean EF was virtually stable with age in males but rose a little with increasing age in females. Conversely, the mean LVMI was virtually stable with age in females but reduced with age in males. The other mean variables (EDVI, ESVI, and SVI) were found to reduce with age by varying amounts. These patterns of change over time are most similar to those reported by Hudsmith et al2 at 1.5T, where they compared cohorts of volunteers in groups with age stratification <35 years and >35 years. Similar patterns of change over time are also presented by Alfakih et al1 and Macieira et al3 at 1.5T.

The normalization of cardiac structure and function data is a complex area, and many approaches have been reported. Normalization to height,25 fat‐free mass (FFM),26 and weight27 have been proposed, but the method most commonly employed is normalization to BSA.28 The Mosteller index to BSA was chosen for this work since it is relatively simple, widely used, and validated on a wide range of subject sizes. Other normalization methods such as that proposed by DuBois and DuBois29 are also available, but this latter study was only validated on very small cohorts and is considered to be less meaningful at the lower and upper ranges of height and weight combinations.

In order to maintain optimal consistency, the study was conducted over a 5‐year period using the same scanner and the same RF coil combinations. No significant downtime was experienced over the duration of the work and the only minor change to the system over this period was an upgrade from software version VB15 to VB17, which did not noticeably affect the functionality of the MR protocols and analysis packages used. To this end, the experimental equipment was considered to remain stable for the duration of the experiment.

The data were acquired over such a large cohort that we elected to use multiple observers for the segmentation analysis. Each observer (six in total) was responsible for segmenting approximately n = 250 datasets. These data were stratified by gender and normalized to BSA (using the Mosteller formula) in order to account for body habitus variations between each of the cohorts allocated to each of the segmentation observers. This approach enables the variations in each LV parameter to be attributable to the segmentation technique, and not be influenced by the physical size or gender distribution of the cohort populations. The mean age of the cohorts allocated to each segmentation observer is also closely matched, which has ensured further consistency, ie, the segmentation technique itself is the dominant factor that forms the variation between observers. Although the segmentation technique was agreed beforehand and all observers were experienced, some real‐world differences between observers were apparent. The mean EF was slightly larger (and the ESVI slightly lower) for volunteers segmented by observer 1, suggesting that the observer was heavily excluding papillary muscles at end‐systole from the ESV blood pool volume. Similarly, the range of values for mean LVMI was variable in places, with observer 4 tending to generate slightly smaller mean values. While some of this variation may be due to the fact that different volunteers were included in each cohort (an accepted limitation of the study design), the likelihood is that it is mostly down to subtle variations in the segmentation technique between the observers (similar variability figures are reported in studies by Chuang et al30 and Suinesiaputra et al31). It was, however, most encouraging to note that the generated data were generally very similar between all of the observers.

The use of a 3.0T scanner for this work was proposed on the basis that the theoretical improvement to the signal‐to‐noise ratio might be traded for faster scanning (ie, more LV SA slices per breath‐hold) and therefore faster volunteer throughput. However, in reality much of the “time saved” (relative to 1.5T) was required for the process of image optimization, eg, the use of optimized volunteer‐specific shimming techniques (eg, “frequency scout”) and targeted shim regions placed over the area of interest during the examination in order to eradicate resonant‐offset banding and flow‐related artifacts. Although relatively little “time‐saving” was achieved, the comparison with existing 1.5T data should help to provide support data for future population‐based research studies that may utilize scanners at both field strengths.

Limitations of the study include the fact that the software used was not easily able to account for papillary muscle volumes, leading to possible small variation between observers as to how the papillary structures would in practice be treated. A further limitation of the study is that no direct equivalent 1.5T data were available and no spoiled gradient echo data were acquired for comparative purposes. The size of the study was such that this was considered prohibitive in terms of time, but similar comparisons are available in the existing literature for 1.5T data12 and it is likely that similar trends would be seen at 3.0T. Finally, it is accepted that the inclusion criteria used to identify certain risk factors associated with CVD during the recruitment phase were a little more relaxed than current recommendations. The decision to select asymptomatic volunteers with a BNP greater than the gender‐specific median was made with a view to undertaking further MR examinations on the same volunteer cohort as they become older. However, the risk of this significantly confounding the results of this study is minimal: we have knowingly excluded anybody with clinically apparent CVD in this work, and in addition to which there were no statistical differences detected within any of the cardiac MR parameters when only those with a BNP <2 SD above the gender‐specific mean (and therefore associated with normal limits) were included in the analysis.

In conclusion, we describe LV reference ranges in a population‐based MR study of volunteers asymptomatic of CVD at 3.0T. The resulting figures are similar to those normal ranges previously reported at 1.5T, and changes with age and gender also follow similar patterns. Data acquired from the full cohort are very similar to those derived from a subgroup with lower risk factors for CVD, suggesting that CVD risk at these levels does not contribute a significant effect. These baseline data might also enable future monitoring of LV changes over time as/when individuals within the cohort require future MR examinations.

Conflict of Interest

The authors declare that they have no competing interests.