Skip to content


  • Poster presentation
  • Open Access

Comparison of different MRI techniques for measuring aortic compliance

  • 1,
  • 1,
  • 1 and
  • 1
Journal of Cardiovascular Magnetic Resonance201012(Suppl 1):P137

Published: 21 January 2010


  • Pulse Wave Velocity
  • Aortic Stiffness
  • Short Processing Time
  • Aortic Pulse Wave Velocity
  • Shallow Breathing


Maintenance of aortic visco-elastic properties is essential for proper physiology(1). Reduced aortic compliance has shown to correlate with different pathologies (e.g. dissection). Invasive techniques using pressure catheters are historically the gold standard for measuring aortic stiffness. Transit-time (TT) (2) and cross-correlation (XC) (3) are recently introduced as MRI techniques for measuring pulse wave velocity (PWV), which reflects the vessel compliance. However, the reproducibility and behavior of these techniques have not yet been studied, which is the purpose of this work.


Thirty consecutive cardiac patients (22 males/8 females; age = 51 ± 15 years), along with five healthy volunteers, were scanned on a 3 T MRI system (Siemens TIM TRIO, Erlangen, Germany). Three velocity-encoded (venc) cine images of the descending aorta were acquired: one ("candy cane") series along the aortic path and two cross-section series separated by about 12 cm. The imaging parameters were: flash sequence; TR/TE = 40/2 ms; flip-angle = 15º; slice-thickness = 8 mm; venc = 150 cm/s; #phases = 128; scan-time = 26 s/slice of shallow breathing.

In-house software was created with Matlab for analyzing the images. Figs. 1 and 2 show images of the same person, where PWV was calculated with TT and XC methods, respectively. Five of the conducted MRI scans were repeated twice with different position markers and scouting to compute the scan-rescan variability. Two experts analyzed the images to determine inter-observer variability. One of the experts analyzed the images twice to compute intra-observer variability. Paired t-tests and Bland-Altman analysis were conducted to measure the difference significance between the results. P < 0.05 was considered statistically significant.
Figure 1
Figure 1

Transit-time method for calculating PWV. Velocity curves (left) are computed at two distant points along the descending aorta (right). PWV = Δx/Δt, where Δx is the distance between the two locations and Δt is the time difference between the two velocity curves.

Figure 2
Figure 2

Cross-correlation method for calculating PWV. Flow patterns (up) are computed at several points along the aorta path (left). Cross correlation is used to estimate the time shift between consecutive points. Linerar least-square fitting is used to calculate PWV (down).


The measured PWV values ranged from 2-16 m/s. The inter-observer/intra-observer variabilities were low as indicated by the interclass correlation-coefficient r = 0.94/0.98 and 0.83/0.87 for the TT and XC methods, respectively. The scan-rescan results did not show significant difference (P > 0.2). The mean differences between the repeated measurements were 0.035 and 0.17 m/s for TT and XC methods, respectively. Bland-Altman analysis (Fig. 3) showed no significant difference between the two methods, with XC slightly overestimating PWV.
Figure 3
Figure 3

Bland-Altman plot for the correlation between TT and XC methods for calculating PWV. The plot shows no significant bias between the two methods. All points lie within the ± 2SD margin.

Discussion and conclusion

The TT and XC methods provide reproducible means of estimating PWV in the aorta. The TT method produced the most reproducible measurements with minimal user interface. The XC method has the shortest processing time and requires only one series of MRI images. Both techniques require high temporal resolution for accurate measurements. In conclusion, either technique can be used for estimating aortic PWV with insignificant difference between the results.

Authors’ Affiliations

Oxford University, Oxford, UK


  1. Dobrin : Phys Rev. 58: 397-460.Google Scholar
  2. Mohiaddin, et al: J Appl Physiol. 74: 492-497.Google Scholar
  3. Fielden, et al: JMRI. 27: 1382-1387. 10.1002/jmri.21387.Google Scholar


© Ibrahim et al; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd.