TY - GEN
T1 - Estimation of aortic pulse wave transit time in MRI using complex wavelet cross-spectrum analysis
AU - Bargiotas, Ioannis
AU - Mousseaux, Elie
AU - Yu, Wen Chung
AU - Venkatesh, Bharath Ambale
AU - Bollache, Emilie
AU - De Cesare, Alain
AU - Lima, Joao A.C.
AU - Redheuil, Alban
AU - Kachenoura, Nadjia
N1 - Publisher Copyright:
© 2015 CCAL.
PY - 2015/2/16
Y1 - 2015/2/16
N2 - Aortic pulse wave velocity (PWV) increases with arterial stiffness and aging and predicts cardiovascular mortality. It is commonly estimated using applanation tonometry at carotid and femoral arterial sites (cfPWV). Although cardiovascular MRI offers reliable segmental measurement of arterial length, accurate transit time (TT) determination between flow curves remains a challenge. We developed a wavelet-based method, which enables temporal localization of signal frequencies, to estimate TT by the weighted phase difference between ascending and descending aorta flow curves. We compared this approach in terms of linear correlations with age, cfPWV and effects of decreasing temporal resolution by factors of 2, 3 and 4, with previous methods which 1) restrict their analysis to systolic upslope (time domain upslopes-archPWVTU) and 2) decompose into harmonics flow curves from the whole cardiac cycle (Fourier-based-archPWVF-robust to low temporal resolution). We studied 71 healthy subjects (45±15 years, 29 females) who underwent MRI velocity acquisitions and cfPWV measurements. Wavelet method provided the highest linear correlations with age and cfPWV and was the most robust to low temporal resolutions. Wavelet method might help to overcome current limitations related to MRI low temporal resolution.
AB - Aortic pulse wave velocity (PWV) increases with arterial stiffness and aging and predicts cardiovascular mortality. It is commonly estimated using applanation tonometry at carotid and femoral arterial sites (cfPWV). Although cardiovascular MRI offers reliable segmental measurement of arterial length, accurate transit time (TT) determination between flow curves remains a challenge. We developed a wavelet-based method, which enables temporal localization of signal frequencies, to estimate TT by the weighted phase difference between ascending and descending aorta flow curves. We compared this approach in terms of linear correlations with age, cfPWV and effects of decreasing temporal resolution by factors of 2, 3 and 4, with previous methods which 1) restrict their analysis to systolic upslope (time domain upslopes-archPWVTU) and 2) decompose into harmonics flow curves from the whole cardiac cycle (Fourier-based-archPWVF-robust to low temporal resolution). We studied 71 healthy subjects (45±15 years, 29 females) who underwent MRI velocity acquisitions and cfPWV measurements. Wavelet method provided the highest linear correlations with age and cfPWV and was the most robust to low temporal resolutions. Wavelet method might help to overcome current limitations related to MRI low temporal resolution.
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U2 - 10.1109/CIC.2015.7411013
DO - 10.1109/CIC.2015.7411013
M3 - Conference contribution
AN - SCOPUS:84964007928
T3 - Computing in Cardiology
SP - 725
EP - 728
BT - Computing in Cardiology Conference 2015, CinC 2015
A2 - Murray, Alan
PB - IEEE Computer Society
T2 - 42nd Computing in Cardiology Conference, CinC 2015
Y2 - 6 September 2015 through 9 September 2015
ER -