• Rui Li, Hosamadin S Assadi, Xiaodan Zhao, Gareth Matthews, Zia Mehmood, Ciaran Grafton-Clarke, Vaishali Limbachia, Rimma Hall, Bahman Kasmai, Marina Hughes, Kurian Thampi, David Hewson, Marianna Stamatelatou, Peter P Swoboda, Andrew J Swift, Samer Alabed, Sunil Nair, Hilmar Spohr, John Curtin, Yashoda Gurung-Koney, GeestRob J van derRJVDepartment of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands., Vassilios S Vassiliou, Liang Zhong, and Pankaj Garg.
• Norwich Medical School, University of East Anglia, Norfolk NR4 7TJ, UK.
• Medicina (Kaunas). 2024 Oct 3; 60 (10).

Abstract(1) Background and Objectives: Flow assessment using cardiovascular magnetic resonance (CMR) provides important implications in determining physiologic parameters and clinically important markers. However, post-processing of CMR images remains labor- and time-intensive. This study aims to assess the validity and repeatability of fully automated segmentation of phase contrast velocity-encoded aortic root plane. (2) Materials and Methods: Aortic root images from 125 patients are segmented by artificial intelligence (AI), developed using convolutional neural networks and trained with a multicentre cohort of 160 subjects. Derived simple flow indices (forward and backward flow, systolic flow and velocity) and complex indices (aortic maximum area, systolic flow reversal ratio, flow displacement, and its angle change) were compared with those derived from manual contours. (3) Results: AI-derived simple flow indices yielded excellent repeatability compared to human segmentation (p < 0.001), with an insignificant level of bias. Complex flow indices feature good to excellent repeatability (p < 0.001), with insignificant levels of bias except flow displacement angle change and systolic retrograde flow yielding significant levels of bias (p < 0.001 and p < 0.05, respectively). (4) Conclusions: Automated flow quantification using aortic root images is comparable to human segmentation and has good to excellent repeatability. However, flow helicity and systolic retrograde flow are associated with a significant level of bias. Overall, all parameters show clinical repeatability.

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