Background
Aortic stiffening is an early sign of remodeling and functional changes in arterial hemodynamics, and a marker of cardiovascular aging [
1,
2]. The clinical usefulness of aortic stiffness has been previously demonstrated through its significant associations with adverse left ventricular (LV) remodeling, coronary heart disease, atherosclerosis and elevated mortality [
3‐
6]. Aortic stiffness is commonly assessed using carotid-femoral (Cf) pulse wave velocity (PWV), which has been shown to be an accurate non-invasive alternative [
7‐
9] to cardiac catheterization [
10] in the in vivo measurement of pulse wave velocity (PWV). PWV is defined as the distance (D) travelled by the pressure wave between two anatomical locations, divided by the transit time (TT) spent by the wave to travel such distance.
Cardiovascular magnetic resonance imaging (CMR) offers excellent anatomical coverage and its anatomical and velocity-encoded sequences allow an accurate estimation of aortic geometry (length, diameters, volumes) as well as blood flow-derived indices in the thoracic aorta. In particular, two-dimensional through-plane phase-contrast CMR (2D phase contrast (PC)-CMR) has been used for the estimation of aortic (ao) PWV (aoPWV), using arch length from the ascending (AAo) to the descending (DAo) aorta, divided by the TT derived from a single acquisition plane positioned perpendicularly to both the AAo and DAo [
11‐
14]. Alternatively, ascending aorta PWV estimation was also proposed using the theoretical Bramwell-Hill (BH) model and aortic distensibility, which is commonly derived from aortic cine CMR and central pulse pressure [
15‐
17].
CMR with full three-dimensional anatomical coverage and velocity encoding in the three directions resolved throughout the cardiac cycle (4D flow CMR) has been developed, opening new and unique opportunities to both visualize and quantify cardiovascular complex blood flow [
18,
19]. 4D flow CMR has several advantages, including its excellent 3D anatomical and velocity coverage which enables an accurate estimation of aortic arch length and aortic flow rates and velocities. Furthermore, as compared with 2D PC-based approaches, 4D flow PWV is better suited to diseases with complex arterial geometry and tortuosity or with heterogeneous stiffness patterns along the arterial tree that can be associated with atherosclerosis [
20] or changes in arterial size [
21,
22]. Moreover, the estimation of aortic PWV from 4D flow CMR [
20] has been shown to be feasible using either TT [
20,
23‐
25] or plane fitting [
20] approaches. The present study aims to provide a comprehensive comparison of both TT and plane fitting-based methods for aortic PWV estimation from 4D flow CMR in healthy subjects, in terms of: 1) associations with the CMR-independent well-established Cf-PWV measure, 4D flow CMR-independent BH-PWV, age and LV mass-to-volume ratio, as well as 2) inter-observer reproducibility and robustness to temporal resolution.
Discussion
This study provides a comparison of the main methods available in the literature for aortic PWV estimation, using 4D flow CMR in 47 healthy subjects. The significant associations with age and the non-invasive reference applanation tonometry-derived Cf-PWV (p < 0.001) demonstrated the consistency of all 4D flow CMR-derived aoPWV estimates. Methods taking into account the whole 3D aortic spatial coverage, specifically the method based on the wavelet transform for transit time estimation were found to be superior to the other approaches, as revealed by 1) their stronger associations with Cf-PWV, Bramwell-Hill model-derived AAo PWV, age, and LV-mass-to-volume ratio as well as 2) their higher reproducibility and robustness to lower reconstructed temporal resolution. Interestingly, correlations of 4D flow CMR aoPWV methods, which account for the full 3D coverage, with age and LV mass-to-volume ratio were in the same range or even slightly higher than those obtained when using Cf-PWV, and remained significant after adjustment for the main confounders.
Aortic PWV values obtained in our study from 4D flow data are in a similar range using all methods. Furthermore, comparison against the widely available 2D PC-CMR aortic arch PWV values estimated in large populations in the literature revealed no substantial differences. Indeed, normal values for aortic arch PWV summarized by Kawel-Boehm et al. [
43] were 3.9 ± 1.1 m/s for age range 30–39 years, 5.6 ± 1.4 m/s for 40–49 years, 7.2 ± 2.3 m/s for 50–59 years, 9.7 ± 2.9 m/s for 60–69 years, 11.1 ± 4.6 m/s for age ≥ 70 years. Normal values for 4D flow CMR-derived aoPWV are not available yet since the majority of previous studies was performed on small groups or pathological individuals while using various methods. Nevertheless, despite differences in population age, our values were in the same range or only slightly higher than those provided in a recent 4D flow study [
44] while using similar TT-based approaches (
n = 8, age = 23 ± 2 years: PWV = 5.7 ± 0.7 m/s when using Fourier analysis and PWV = 5.5 ± 0.7 m/s when using cross-correlation;
n = 8, age = 58 ± 2 years: PWV = 9.3 ± 1.3 m/s when using Fourier analysis and PWV = 8.9 ± 1.4 m/s when using cross-correlation).
Our choice of the tested methods for aoPWV estimation in this study was based on previous 2D PC-CMR and 4D flow CMR studies. Indeed they have demonstrated that: 1) approaches based on a single point of the flow curve (foot or peak) are hampered by low velocity to noise ratio or by the effects of wave reflection [
24,
36,
45], 2) frequency and time-frequency domain methods for TT estimation are more robust to low temporal resolution [
37,
38], and 3) approaches taking into account volumetric flow data along the aorta are more robust than methods considering only two measurement sites [
20,
24]. The high reproducibility of PWV previously shown in 2D PC-CMR studies was not found in our 2D-like strategy (S1), which resulted in sizeable Bland-Altman limits of agreements in our data. Such lower performances of the 2D-like strategies in our study might be explained by the static nature of the 4D flow CMR aortic lumen segmentation through time, contrary to an automated dynamic time-resolved segmentation in 2D PC-CMR, which would hamper the AAo velocity curves, as well as by the lower spatial resolution of 4D flow as compared to 2D PC-CMR data, which would highly affect the distal descending aorta because of its small cross-section.
In agreement with previous findings [
36,
37], systolic upslope-based methods were more consistent than wave-based methods especially when using the 3D strategy, as revealed by the comparison between wavelet or cross-correlation and Fourier approaches in terms of associations with age and LV mass to volume ratio. This can be explained by the fact that the early systolic upslope is less distorted by wave reflexion than late systolic and diastolic phases of the flow curve [
35,
36]. Besides, methods considering the volumetric coverage of 4D flow CMR data were more reliable than those based on two planes only.
Original features of our study include semi-automated segmentation of the aorta and an automated positioning of flow measurement planes perpendicular to the centreline. In addition, comparisons against the CMR-independent tonometry Cf-PWV and 4D flow CMR-independent ascending aortic BH-PWV measures, as well as the assessment of physiological associations with LV mass-to-volume ratio and age were reported. Our aoPWV measurements confirmed physiological knowledge on arterial stiffening gradient from the central aorta to peripheral arteries, resulting in the lowest values for the ascending aortic Bramwell-Hill method, intermediate values for the 4D flow CMR methods over the whole aorta, and the highest values for the carotid to femoral arteries PWV. This stiffness gradient phenomenon was more marked in younger subjects with highly elastic central arteries than in elderly subjects, because of stiffness homogenisation from central arteries towards the periphery with ageing.
Furthermore, comparison to Cf-PWV and BH-PWV revealed the superiority of the plane fitting method as well as wavelet TT-based approach previously shown using 2D-PC CMR to be more robust to low temporal resolution [
37]. Our findings further confirm such robustness of the wavelet-based method to 20- vs. 50-reconstructed time frames. In addition, such method was strongly associated with age even in subjects ≥50 years, in whom we expect increased aortic stiffness and thus decreased TT when compared to younger subjects. Although the wavelet-based approach resulted in the highest performances when compared to the plane fitting approach, its implementation requires tuning of parameters such as the mother wavelet and sampling frequency. However, one might highlight that the same parameters than those previously described using 2D-PC CMR data [
37] were used in each subject for the wavelet method in the present study.
Reliability of our 4D flow CMR aoPWV measurement was also demonstrated by the positive association with LV mass-to-volume ratio. Indeed, it is known that in the process of healthy aging, arterial stiffening is associated with increased LV afterload and subsequent LV hypertrophic remodeling, which was measured in our study through the LV mass to end-diastolic volume ratio [
30]. Such association was independent of age, systolic blood pressure, BMI, and gender, revealing that 4D flow CMR aoPWV is an independent marker of LV alteration in the process of healthy aging. Gender was also an independent correlate of LV mass-to-volume ratio, in agreement with a previous study [
46]. Accordingly, evaluation of aortic stiffness using 4D flow CMR can provide mechanistic knowledge to improve understanding of ventricular-arterial coupling.
The main limitation of our study is the lack of a direct aortic PWV gold standard measurement as provided by catheterization [
10]. However such invasive procedure was not feasible in healthy subjects. Moreover, associations with the non-invasive reference Cf-PWV [
1,
7,
29], and with physiological criteria such as age and LV mass-to-volume ratio were used to compare the performances of 4D flow CMR aoPWV methods. Since 4D flow CMR is limited by low spatial resolution and signal to noise ratio for low velocities, time-resolved segmentation was not available. Accordingly, a fixed segmentation on an enhanced aortic angiogram, reconstructed from peak systolic phases, was used along with mean velocity curves rather than flow curves. In the future, a time-resolved segmentation would be necessary for an accurate estimation of aortic flow along the cardiac cycle. Such segmentation would require an evolvement of 4D flow CMR sequences with the use of higher spatial resolution and multiple encoding velocities to improve velocity to noise ratio especially in regions with low velocities or the use of motion-compensated compressed-sensing techniques which were shown to improve the quality of 4D flow images [
47,
48]. To enhance contrast and signal to noise ratio, contrast agent is often used in 4D flow CMR. However, since standard CMR exams in clinical routine usually include LGE and/or post-contrast T1, 4D flow CMR acquisition can be interleaved while waiting for such tissue characterization sequences to be acquired. Another limitation is the lack of test-retest variability assessment of the 4D flow CMR aoPWV due to non-available data. However, one might highlight that the main goal of our study was to isolate the technical reliability of 4D flow CMR aoPWV from the robustness of 4D flow CMR to hemodynamic changes over time. Thus, the proposed 4D flow CMR techniques were rather compared in terms of inter-observer reproducibility and associations with 4D flow CMR-independent aoPWV measures such as the tonometric Cf-PWV and the cine bSSFP-derived BH-PWV as well as physiological association with age and LV mass-to-volume ratio.
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