ArticleJournal of medical imaging (Bellingham, Wash.)2016
Automated pericardial fat quantification from coronary magnetic resonance angiography: feasibility study.
Article in Journal of medical imaging (Bellingham, Wash.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Epicardial and pericardial adipose tissue: anatomy, physiology, imaging, segmentation, and treatment effects.The British journal of radiology · 2026Review
- Automated Quality-Controlled Cardiovascular Magnetic Resonance Pericardial Fat Quantification Using a Convolutional Neural Network in the UK Biobank.Frontiers in cardiovascular medicine · 2021Article
- State-of-the-art review article. Atherosclerosis affecting fat: What can we learn by imaging perivascular adipose tissue?Journal of cardiovascular computed tomographyReview
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Authors and funding
11 authors.
Funding
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Abstract
Pericardial fat volume (PFV) is emerging as an important parameter for cardiovascular risk stratification. We propose a hybrid approach for automated PFV quantification from water/fat-resolved whole-heart noncontrast coronary magnetic resonance angiography (MRA). Ten coronary MRA datasets were acquired. Image reconstruction and phase-based water-fat separation were conducted offline. Our proposed algorithm first roughly segments the heart region on the original image using a simplified atlas-based segmentation with four cases in the atlas. To get exact boundaries of pericardial fat, a three-dimensional graph-based segmentation is used to generate fat and nonfat components on the fat-only image. The algorithm then selects the components that represent pericardial fat. We validated the quantification results on the remaining six subjects and compared them with manual quantifications by an expert reader. The PFV quantified by our algorithm was [Formula: see text], compared to [Formula: see text] by the expert reader, which were not significantly different ([Formula: see text]) and showed excellent correlation ([Formula: see text],[Formula: see text]). The mean absolute difference in PFV between the algorithm and the expert reader was [Formula: see text]. The mean value of the paired differences was [Formula: see text] (95% confidence interval: [Formula: see text] to 6.21). The mean Dice coefficient of pericardial fat voxels was [Formula: see text]. Our approach may potentially be applied in a clinical setting, allowing for accurate magnetic resonance imaging (MRI)-based PFV quantification without tedious manual tracing.
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