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


Journal

Magnetic resonance imaging
ISSN: 1873-5894
Titre abrégé: Magn Reson Imaging
Pays: Netherlands
ID NLM: 8214883

Informations de publication

Date de publication:
06 2020
Historique:
received: 05 11 2019
revised: 21 02 2020
accepted: 23 02 2020
pubmed: 28 2 2020
medline: 16 12 2020
entrez: 28 2 2020
Statut: ppublish

Résumé

MRI 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. Phase 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. Group 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%. Group 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.

Sections du résumé

BACKGROUND
MRI 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.
METHODS
Phase 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.
RESULTS
Group 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%.
CONCLUSION
Group 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.

Identifiants

pubmed: 32105671
pii: S0730-725X(19)30659-9
doi: 10.1016/j.mri.2020.02.013
pii:
doi:

Types de publication

Journal Article Validation Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

8-15

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Auteurs

Niema M Pahlevan (NM)

Department of Aerospace & Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA; Advanced Imaging and Spectroscopy Center, Huntington Medical Research Institutes, Pasadena, CA 91105, USA. Electronic address: pahlevan@usc.edu.

Timothy Yao (T)

Advanced Imaging and Spectroscopy Center, Huntington Medical Research Institutes, Pasadena, CA 91105, USA.

Karen Chu (K)

Advanced Imaging and Spectroscopy Center, Huntington Medical Research Institutes, Pasadena, CA 91105, USA. Electronic address: karen.chu@hmri.org.

Soren Cole (S)

Department of Aerospace & Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA; Advanced Imaging and Spectroscopy Center, Huntington Medical Research Institutes, Pasadena, CA 91105, USA. Electronic address: sc_108@usc.edu.

Thao Tran (T)

Advanced Imaging and Spectroscopy Center, Huntington Medical Research Institutes, Pasadena, CA 91105, USA. Electronic address: thao.tran@hmri.org.

John C Wood (JC)

Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA; Division of Pediatric Cardiology, Children's Hospital Los Angeles, CA 90027, USA. Electronic address: JWood@chla.usc.edu.

Kevin S King (KS)

Advanced Imaging and Spectroscopy Center, Huntington Medical Research Institutes, Pasadena, CA 91105, USA. Electronic address: kevin.king@hmri.org.

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