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18 February 2016 Automated pericardial fat quantification from coronary magnetic resonance angiography: feasibility study
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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 62.78±27.85  cm3, compared to 58.66±27.05  cm3 by the expert reader, which were not significantly different (p=0.47) and showed excellent correlation (R=0.89,p<0.01). The mean absolute difference in PFV between the algorithm and the expert reader was 9.9±8.2  cm3. The mean value of the paired differences was −4.13  cm3 (95% confidence interval: −14.47 to 6.21). The mean Dice coefficient of pericardial fat voxels was 0.82±0.06. Our approach may potentially be applied in a clinical setting, allowing for accurate magnetic resonance imaging (MRI)-based PFV quantification without tedious manual tracing.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2016/$25.00 © 2016 SPIE
Xiaowei Ding, Jianing Pang, Zhou Ren, Mariana Diaz-Zamudio, Chenfanfu Jiang, Zhaoyang Fan, Daniel S. Berman, Debiao Li, Demetri Terzopoulos, Piotr J. Slomka, and Damini Dey "Automated pericardial fat quantification from coronary magnetic resonance angiography: feasibility study," Journal of Medical Imaging 3(1), 014002 (18 February 2016).
Published: 18 February 2016

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