Measurement of aortic arch pulse wave velocity in cardiovascular MR: comparison of transit time estimators and description of a new approach.

Purpose: To investigate the efficiency of a new method (TT-Upslope) for transit time (Δt) estimation from cardiovascular MR (CMR) velocity curves.

Materials And Methods: Fifty healthy volunteers (40 ± 15 years) underwent applanation tonometry to estimate carotid-femoral pulse wave velocity (cf-PWV) and carotid pressure measurements, and CMR to estimate aortic arch-PWV and ascending aorta distensibility (AAD). The Δt was calculated with TT-Upslope by minimizing the area delimited by two sigmoid curves fitted to the systolic upslope of the ascending (AAC) and descending (DAC) aorta velocity curves, and compared with previously described methods: TT-Point using the half maximum of AAC and DAC, TT-Foot using AAC and DAC feet, and TT-Wave by minimizing the area between AAC and DAC curves using cross correlation.

Consistency of aortic distensibility and pulse wave velocity estimates with respect to the Bramwell-Hill theoretical model: a cardiovascular magnetic resonance study.

Background: Arterial stiffness is considered as an independent predictor of cardiovascular mortality, and is increasingly used in clinical practice. This study aimed at evaluating the consistency of the automated estimation of regional and local aortic stiffness indices from cardiovascular magnetic resonance (CMR) data.

Results: Forty-six healthy subjects underwent carotid-femoral pulse wave velocity measurements (CF_PWV) by applanation tonometry and CMR with steady-state free-precession and phase contrast acquisitions at the level of the aortic arch.

Automated segmentation of the aorta from phase contrast MR images: validation against expert tracing in healthy volunteers and in patients with a dilated aorta.

Purpose: To assess if segmentation of the aorta can be accurately achieved using the modulus image of phase contrast (PC) magnetic resonance (MR) acquisitions.

Materials And Methods: PC image sequences containing both the ascending and descending aorta of 52 subjects were acquired using three different MR scanners. An automated segmentation technique, based on a 2D+t deformable surface that takes into account the features of PC aortic images, such as flow-related effects, was developed.