Group delay method for MRI aortic pulse wave velocity measurements in clinical protocols with low temporal resolution: Validation in a heterogeneous cohort

Abstract

BackgroundMRI assessment of aortic pulse wave velocity (PWV) helps predict the risk of vascular events, but the recommended phase contrast sampling rate is faster than what is utilized in most clinical sequences. There are many existing MRI databases obtained for assessment of cardiac output using lower temporal frequency sampling where information might be obtained about aortic stiffness (PWV). In this work, we sought to evaluate whether the Group Delay (GD) method can generate a reproducible measure of stiffness and describe expected age-related stiffening of the aortic arch using lower sampling rates in standard clinical sequences. MethodsPhase contrast (PC) MRI was obtained on the ascending and descending aortic arch in a heterogeneous adult cohort (n = 23; 9 women) spanning over a wide range of ages (ages 24–89, mean 49.4 ± 18.4). Data was collected with standard cardiac MRI protocols for cardiac output evaluation (repetition time = 7.8 ms, views-per-segment = 4, encoding velocity = 200 cm/s). Pulse wave transit times (TT) were computed using the GD method, two other validated automated approaches (cross correlation TT Algorithm by Gaddum and Segment by Medviso), and the manual tangent method. Pressure waveforms from tonometry and flow waveforms from PC MRI were used to assess wave reflections. ResultsGroup Delay and TT-Algorithm showed significant and high retest reproducibility (r = 0.86 for both) as well as high PWV correlation with age (r = 0.93, P-value < 0.00005 and r = 0.96, P-value < 0.00005 respectively) and with each other (r = 0.94, P-value < 0.00001, RMSE = 0.94 m/s). Arbitrary altering of the image acquisition trigger in the GD method introduced error of 10%–13%, but the TT-algorithm error range was 11%–25%. ConclusionGroup Delay enables reproducible assessment of transit time to derive PWV from low temporal resolution clinical cardiac MRI sequences that can also identify age-related stiffening.