Automated quantification of simple and complex aortic flow using 2D phase contrast MRI

Li, Rui, Assadi, Hosamadin S. ORCID: https://orcid.org/0000-0002-6143-8095, Zhao, Xiaodan, Matthews, Gareth ORCID: https://orcid.org/0000-0001-8353-4806, Mehmood, Zia, Grafton-Clarke, Ciaran ORCID: https://orcid.org/0000-0002-8537-0806, Limbachia, Vaishali, Hall, Rimma, Kasmai, Bahman, Hughes, Marina, Thampi, Kurian, Hewson, David, Stamatelatou, Marianna, Swoboda, Peter P., Swift, Andrew J., Alabed, Samer, Nair, Sunil, Spohr, Hilmar, Curtin, John, Gurung-Koney, Yashoda, van der Geest, Rob J., Vassiliou, Vassilios S. ORCID: https://orcid.org/0000-0002-4005-7752, Zhong, Liang and Garg, Pankaj ORCID: https://orcid.org/0000-0002-5483-169X (2024) Automated quantification of simple and complex aortic flow using 2D phase contrast MRI. Medicina, 60 (10). ISSN 1648-9144

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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.

Item Type:	Article
Additional Information:	Data Availability Statement: Underlying data: access to the raw images of patients is not permitted since specialized post-processing imaging-based solutions can identify the study patients in the future. Funding information: This research was funded by Wellcome Trust Clinical Research Career Development Fellowships (220703/Z/20/Z, September 2021). The funders had no role in study design, data collection and analysis, publication decisions, or manuscript preparation. Rights retention statement: For the purpose of open access, this author has applied for a CC BY public copyright license to any author-accepted manuscript version arising from this submission.
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Cardiovascular and Metabolic Health
Depositing User:	LivePure Connector
Date Deposited:	03 Oct 2024 15:30
Last Modified:	06 Oct 2024 06:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/96873
DOI:	10.3390/medicina60101618

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