Automated segmental analysis of fully quantitative myocardial blood flow maps by first-pass perfusion cardiovascular magnetic resonance

Jacobs, M., Benovoy, M., Chang, L.-C., Corcoran, D., Berry, C. , Arai, A. E. and Hsu, L.-Y. (2021) Automated segmental analysis of fully quantitative myocardial blood flow maps by first-pass perfusion cardiovascular magnetic resonance. IEEE Access, 9, pp. 52796-52811. (doi: 10.1109/ACCESS.2021.3070320) (PMID:33996344) (PMCID:PMC8117952)

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Abstract

First pass gadolinium-enhanced cardiovascular magnetic resonance (CMR) perfusion imaging allows fully quantitative pixel-wise myocardial blood flow (MBF) assessment, with proven diagnostic value for coronary artery disease. Segmental analysis requires manual segmentation of the myocardium. This work presents a fully automatic method of segmenting the left ventricular myocardium from MBF pixel maps, validated on a retrospective dataset of 247 clinical CMR perfusion studies, each including rest and stress images of three slice locations, performed on a 1.5T scanner. Pixel-wise MBF maps were segmented using an automated pipeline including region growing, edge detection, principal component analysis, and active contours to segment the myocardium, detect key landmarks, and divide the myocardium into sectors appropriate for analysis. Automated segmentation results were compared against a manually defined reference standard using three quantitative metrics: Dice coefficient, Cohen Kappa and myocardial border distance. Sector-wise average MBF and myocardial perfusion reserve (MPR) were compared using Pearson’s correlation coefficient and Bland-Altman Plots. The proposed method segmented stress and rest MBF maps of 243 studies automatically. Automated and manual myocardial segmentation had an average (± standard deviation) Dice coefficient of 0.86 ± 0.06, Cohen Kappa of 0.86 ± 0.06, and Euclidian distances of 1.47 ± 0.73 mm and 1.02 ± 0.51 mm for the epicardial and endocardial border, respectively. Automated and manual sector-wise MBF and MPR values correlated with Pearson’s coefficient of 0.97 and 0.92, respectively, while Bland-Altman analysis showed bias of 0.01 and 0.07 ml/g/min. The validated method has been integrated with our fully automated MBF pixel mapping pipeline to aid quantitative assessment of myocardial perfusion CMR.

Item Type:Articles
Additional Information:This work was supported by the Intramural Research Program of the National Heart, Lung and Blood Institute under Grant ZIA HL006137-08. The work of Colin Berry was supported by the British Heart Foundation under Grant PG/17/25-32884 and Grant RE/18/6134217.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Corcoran, Dr David and Berry, Professor Colin
Authors: Jacobs, M., Benovoy, M., Chang, L.-C., Corcoran, D., Berry, C., Arai, A. E., and Hsu, L.-Y.
College/School:College of Medical Veterinary and Life Sciences > Institute of Cardiovascular and Medical Sciences
Journal Name:IEEE Access
Publisher:IEEE
ISSN:2169-3536
ISSN (Online):2169-3536
Published Online:01 April 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in IEEE Access 9: 52796-52811
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
301454CORonary MICrovascular Angina (CorMicA): a pilot trial with a nested MRI sub-studyColin BerryBritish Heart Foundation (BHF)PG/17/25/32884CAMS - Cardiovascular Science
303944BHF Centre of ExcellenceRhian TouyzBritish Heart Foundation (BHF)RE/18/6/34217CAMS - Cardiovascular Science