Image Processing

Automated pericardial fat quantification from coronary magnetic resonance angiography: feasibility study

[+] Author Affiliations
Xiaowei Ding

Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Department of Biomedical Sciences, 8700 Beverly Boulevard, Los Angeles, California 90048, United States

University of California–Los Angeles, Computer Science Department, Computer Graphics & Vision Laboratory, 580 Portola Plaza, Los Angeles, California 90095, United States

Jianing Pang, Zhaoyang Fan

Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Department of Biomedical Sciences, 8700 Beverly Boulevard, Los Angeles, California 90048, United States

Zhou Ren, Chenfanfu Jiang, Demetri Terzopoulos

University of California–Los Angeles, Computer Science Department, Computer Graphics & Vision Laboratory, 580 Portola Plaza, Los Angeles, California 90095, United States

Mariana Diaz-Zamudio

Cedars-Sinai Medical Center, Departments of Imaging and Medicine, 8700 Beverly Boulevard, Los Angeles, California 90048, United States

Daniel S. Berman, Piotr J. Slomka

Cedars-Sinai Medical Center, Departments of Imaging and Medicine, 8700 Beverly Boulevard, Los Angeles, California 90048, United States

University of California–Los Angeles, Department of Medicine, David-Geffen School of Medicine, 10833 Le Conte Avenue, Los Angeles, California 90095, United States

Debiao Li, Damini Dey

Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Department of Biomedical Sciences, 8700 Beverly Boulevard, Los Angeles, California 90048, United States

University of California–Los Angeles, Department of Medicine, David-Geffen School of Medicine, 10833 Le Conte Avenue, Los Angeles, California 90095, United States

J. Med. Imag. 3(1), 014002 (Feb 18, 2016). doi:10.1117/1.JMI.3.1.014002
History: Received September 15, 2015; Accepted January 22, 2016
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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 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.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Xiaowei Ding ; Jianing Pang ; Zhou Ren ; Mariana Diaz-Zamudio ; Chenfanfu Jiang, et al.
"Automated pericardial fat quantification from coronary magnetic resonance angiography: feasibility study", J. Med. Imag. 3(1), 014002 (Feb 18, 2016). ; http://dx.doi.org/10.1117/1.JMI.3.1.014002


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