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Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification

Published Online: 2019-01-07

Published Print: 2019-12

Authors

Bratt, Alex

Kim, Jiwon

Pollie, Meridith

Beecy, Ashley N.

Tehrani, Nathan H.

Codella, Noel

Perez-Johnston, Rocio

Palumbo, Maria Chiara

Alakbarli, Javid

Colizza, Wayne

Drexler, Ian R.

Azevedo, Clerio F.

Kim, Raymond J.

Devereux, Richard B.

Weinsaft, Jonathan W. http://orcid.org/0000-0002-8386-0297
Funding

Funding for this research was provided by:

National Institutes of Health (1R01HL128278-01, K23HL102249, NIH 1K23 HL140092-01)
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Text and Data Mining valid from 2019-01-07

Version of Record valid from 2019-01-07
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Article History

Received: 29 May 2018

Accepted: 18 November 2018

First Online: 7 January 2019

Ethics approval and consent to participate

: Data usage for this study was approved by the Institutional Review Boards at New York Presbyterian Hospital/Weill Cornell Medicine and Duke University. Subjects in the Cornell validation cohort provided written informed consent for research participation; subjects in the Duke validation cohort comprised use of pre-existing CMR data for patients undergoing imaging for clinical purposes.

: Not applicable.

: The authors declare that they have no competing interests.

: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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