期刊
IEEE ACCESS
卷 9, 期 -, 页码 52796-52811出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3070320
关键词
Myocardium; Image segmentation; Imaging; Image edge detection; Pipelines; Blood flow; Lung; Cardiovascular magnetic resonance; myocardial perfusion imaging; myocardial blood flow; image segmentation
资金
- Intramural Research Program of the National Heart, Lung and Blood Institute [ZIA HL006137-08]
A fully automatic method of segmenting left ventricular myocardium from MBF pixel maps was developed and validated on a dataset of clinical CMR perfusion studies. The automated segmentation method showed high consistency and accuracy when compared to manually defined reference standards, with good correlation and minimal bias in sector-wise MBF and MPR values. This validated method has the potential to aid in quantitative assessment of myocardial perfusion in CMR studies.
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.
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