Extracellular volume quantification by cardiac magnetic resonance imaging without hematocrit sampling

Over the last decade cardiovascular magnetic resonance imaging (CMR) has gained increasing importance in clinical practice. While echocardiography remains the standard cardiac imaging tool due to its broad availability and low cost, CMR is now the recognized reference method for evaluation of left and right ventricular size and function [1]. In addition, CMR allows visualization of focal fibrosis and focal expansion of the extracellular space through late gadolinium-enhanced (LGE) imaging [2]; however, LGE does not allow a quantitative analysis of diffuse fibrosis, which may affect large parts of the myocardium. By using recently described T1-mapping methods it is now possible to non-invasively calculate the extracellular volume (ECV), [3, 4], which has been shown to accurately reflect diffuse myocardial fibrosis when compared with biopsy samples [4‐14]. The use of T1-mapping and ECV have been investigated in a broad range of cardiovascular diseases, including valvular heart disease [4, 7, 8, 13], cardiac amyloidosis [15, 16], myocarditis [17, 18], myocardial infarction [19], Anderson-Fabry disease [20] and heart failure [21, 22]. Several studies reported a strong association of ECV with stages of disease and cardiovascular outcomes [11, 23‐25]; however, the hematocrit is needed to calculate the ECV, which limits its routine application and automatic post-scanning processing.

Recently, Treibel et al. demonstrated a linear relationship of hematocrit and blood relaxivity (R1 = 1/blood pool T1 time), which was used to develop a synthetic hematocrit and ECV [25]. The authors postulated that synthetic ECV allows reliable noninvasive quantification of myocardial ECV without blood sampling. So far, that is the only working group that has evaluated the accurateness of synthetic ECV. The aim of the present study was to develop and test a formula for the calculation of synthetic ECV at our center.

A total of 513 patients from our prospective T1-mapping registry were included in this study. All patients provided written informed consent. The ethics committee approved both the registry as well as the current study protocol.

Among all 513 consecutive patients (mean age 57.4 ± 17.5 years old, 49.1% female) mean conventionally measured hematocrit was 39.9 ± 4.7% (range: 18.3–59.8%), native T1-time of LV blood pool was 1570.6 ± 117.8 ms (range: 1193–1982 ms), and conventional ECV was 28.4 ± 6.8% (range: 18.3–88.2%). The derivation cohort consisted of 200 randomly chosen patients (57.7 ± 17.5 years old, 50.0% female). Baseline characteristics (Table 1) did not differ between derivation and validation cohort. Of note, conventional hematocrit, native T1-times of the myocardium and the blood pool, and conventional ECV were similar among the groups (40.2 ± 4.4% versus 39.9 ± 4.8%, 991.4 ± 54.4 ms versus 990.4 ± 56.0 ms, 1561.4 ± 101.8 ms versus 1576.5 ± 126.7 ms, 28.8 ± 7.8% versus 28.2 ± 6.1%, respectively, p > 0.05 for all). Table 1 displays the baseline characteristics stratified by derivation and validation cohort. A significant correlation between R1 and conventional hematocrit (r = 0.592, r² = 0.350, p < 0.001) was found in the derivation cohort. By linear regression analysis the following term was derived to best estimate the hematocrit using R1:

The conventional hematocrit in the validation cohort was similar to the synthetic hematocrit with a close correlation (39.9 ± 4.8% versus 39.9 ± 3.3%, r = 0.533, r² = 0.284, p < 0.001). Similar results were obtained with respect to ECV. Synthetic ECV was almost identical to conventional ECV and highly correlated (28.8 ± 6.1% versus 28.0 ± 6.0%, r = 0.943, r² = 0.889, p < 0.001). Interobserver variability (two independent investigators) of synthetic hematocrit values and native blood pool T1-time measurements was low with ICCs of 0.899 and 0.849, respectively. In the entire cohort synthetic ECV was 28.3 ± 6.6% and showed an excellent correlation with conventional ECV, which was 28.4 ± 6.8% on average (r = 0.959, r² = 0.920, p < 0.001, Fig. 2). By Bland-Altman analysis we found good agreement between conventional and synthetic ECV (mean difference: 0.007%, limits of agreement: −4.32 and 4.33%, Fig. 3).

Treibel et al. [25] proposed a slightly different equation for the calculation of synthetic hematocrit:

When using this formula, synthetic hematocrit and ECV in our cohort would be 43.1 ± 4.2% and 27.0 ± 6.8%, respectively, and would result in also significant, yet slightly weaker, correlations with conventional hematocrit and ECV (r = 0.523, r² = 0.274, p < 0.001, and r = 0.950, r² = 0.903, p < 0.001, respectively).

When stratifying patients according to left ventricular ejection fraction (LVEF < 50% versus ≥ 50%) we found no differences in conventional hematocrit (40.4 ± 5.0% versus 39.8 ± 4.7%, p = 0.871), synthetic hematocrit (39.6 ± 3.4% versus 40.1 ± 3.0%, p = 0.143), or native blood pool T1-times (1590.2 ± 131.3 ms versus 1568.2 ± 116.0 ms); however, patients with reduced ejection fraction had a higher ECV, both using the conventional (30.7 ± 10.0% versus 28.0 ± 6.2%, p = 0.013) and synthetic approach (31.2 ± 9.9% versus 27.8 ± 5.9%, p = 0.001).

It has previously been shown that synthetic ECV using blood pool T1-times for hematocrit calculations correlates well with the ECV based on venous blood hematocrit (conventional ECV); however, these data were derived in one single center in the UK. As CMR referral diagnoses, T1 mapping sequences and patient background may differ among countries and centers, we aimed to test the use of synthetic ECV at our institution in 513 patients using a derivation cohort of 200 and a validation cohort of 313 individuals.

The T1-mapping technique has evolved from a research-only tool to a standard sequence that can now be used in everyday clinical practice. It allows a pixel-by-pixel non-invasive quantification of longitudinal (T1) relaxation times. Several T1-mapping methods are currently available [26‐28]. For all sequences, satisfactory reproducibility has been reported [29].

So far, several studies have compared results of T1-mapping with histological findings of left ventricular biopsies and found that ECV reflects the amount of diffuse myocardial fibrosis in patients with aortic stenosis [4, 7, 8, 13], hypertrophic cardiomyopathy [4, 10], heart failure [9, 11, 30], dilated cardiomyopathy [6, 12] and mixed patient populations [14]. Furthermore, a small number of studies investigated the impact of T1-mapping on survival and found that higher amounts of diffuse fibrosis are associated with an increased event rate [11, 14, 23, 30‐32]. Particularly in infiltrative diseases, T1-mapping has evolved as an important diagnostic tool. In patients with cardiac amyloidosis, native T1-times [16, 33] and ECV [15, 34] were found to often be markedly higher than in other conditions associated with left ventricular hypertrophy [20]. In contrast, due to the low T1-times of fat, low T1-times within the myocardium are a landmark imaging feature in Anderson-Fabry disease where sphingolipids accumulate [20]. Most studies focused on the presence of diffuse fibrosis as the main contributor to myocardial ECV; however, acute or chronic inflammation also alters the extracellular space [35]. It has been shown recently that T1 times are influenced by the presence of myocardial inflammation [36].

One important factor limiting the routine use of ECV in daily practice is the need of the hematocrit for its calculation. Recently, the use of an estimated synthetic hematocrit, based on blood pool T1 time, was proposed [25]. Synthetic ECV showed excellent correlation and agreement with conventional ECV. These results were observed in one single center using one vendor system and it has previously been agreed that T1-mapping results are not directly comparable among centers [37]. Most recently, the same working group reported similar results using different CMR scanners in a pooled analysis of both 1.5 and 3 T [38]. They also investigated synthetic ECV by cardiac computed tomography (CT) and reported excellent results [39]; however, it has to be noted that ECV by CT has so far only been studied to a far lesser extent [40]. Another prospective multicenter study is currently evaluating comparability of T1 mapping results among different CMR units worldwide, using T1-phantoms [41].

In the present cohort we found an excellent correlation between conventional and synthetic ECV by using the formula proposed by Treibel et al. [25]; however, when we used a formula derived from our own patient population, even better agreement between conventional and synthetic ECV was observed. Whether these results are transferable to other centers, which potentially use different T1 mapping sequences and different scanners, remains unknown. Further multicenter trials will be needed to clarify the broad applicability of automatic ECV mapping without the need of conventional hematocrit values.

Synthetic ECV, using native blood pool T1-times to calculate the hematocrit, is feasible and leads to almost identical results in comparison with the conventional method. It may allow fully automatic ECV mapping and thus enable broader use of ECV by CMR T1 mapping in clinical practice.