Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification.
Journal:
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Published Date:
Jan 7, 2019
Abstract
BACKGROUND: Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow.
Authors
Keywords
Aged
Aorta
Aortic Valve
Automation
Blood Flow Velocity
Female
Heart Diseases
Hemodynamics
Humans
Image Interpretation, Computer-Assisted
Machine Learning
Magnetic Resonance Imaging, Cine
Male
Middle Aged
Myocardial Perfusion Imaging
Predictive Value of Tests
Proof of Concept Study
Prospective Studies
Reproducibility of Results
Retrospective Studies
United States