Automated pericardium delineation and epicardial fat volume quantification from noncontrast CT

Ding, Xiaowei; Terzopoulos, Demetri; Díaz-Zamudio, Mariana; Berman, Daniel S.; Slomka, Piotr J.; Dey, Damini

doi:10.1118/1.4927375

Cited by 34 publications

(32 citation statements)

References 37 publications

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“…Scans were read to determine coronary artery calcium (CAC) scores using the Agatston method and EAT volume quantification by an automated algorithm previously described. 14,15 The CAC score and EAT volume were determined previous to the chirurgical procedures.…”

Section: Computed Tomography For Cac Score and Fat Volume Quantificationmentioning

confidence: 99%

<p>Bone Morphogenetic Protein-2 and Osteopontin Gene Expression in Epicardial Adipose Tissue from Patients with Coronary Artery Disease Is Associated with the Presence of Calcified Atherosclerotic Plaques</p>

Luna-Luna¹,

Críales-Vera²,

Medina-Leyte³

et al. 2020

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It has been proposed that the cardiovascular effects of obesity are related to epicardial adipose tissue (EAT), which seems to play an active role on the development and calcification of atherosclerotic plaques, but the mechanisms are still unknown. Therefore, the aim of this study was to determine whether the EAT expresses the genes of calcifying factors and whether such expression is associated with the body mass index (BMI) and with the presence of coronary artery calcium (CAC) in patients with coronary artery disease (CAD). Patients and Methods: Forty-three patients with CAD were enrolled specifically for this study, and their CAC score and EAT volume were determined by computed tomography. As the group of comparison, 41 patients with aortic valve stenosis and CAC = 0 were included (control group). A representative subgroup of 16 CAD patients and 23 controls were selected to obtain EAT biopsies during the chirurgical procedure from the atrio-interventricular groove. The mRNA expression of bone morphogenetic protein-2 and-4 (BMP-2, BMP-4), osteopontin (OPN), osteonectin (ON), and osteoprotegerin (OPG) in EAT was determined by qPCR. Results: The gene expression of OPN and BMP-2 was 70% and 52% higher in the EAT from CAD patients than that in controls, respectively, whereas the expression of OPG, ON, and BMP-4 was similar in both groups. The EAT volume positively correlated with OPG and with the BMI, suggesting a relationship of obesity with local higher expression of calcifying genes in the coronary territory. The logistic regression analysis showed that high levels of both OPN and BMP-2 increased about 6 and 8 times the odds of coronary calcification (CAC score > 0), respectively. Conclusion: EAT correlated with BMI and expressed the mRNA of calcifying genes but only OPN and BMP-2 expression was higher in CAD patients. Higher levels of both OPN and BMP-2 statistically determined the presence of calcium in coronary arteries of CAD patients.

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Section: Computed Tomography For Cac Score and Fat Volume Quantificationmentioning

confidence: 99%

<p>Bone Morphogenetic Protein-2 and Osteopontin Gene Expression in Epicardial Adipose Tissue from Patients with Coronary Artery Disease Is Associated with the Presence of Calcified Atherosclerotic Plaques</p>

Luna-Luna¹,

Críales-Vera²,

Medina-Leyte³

et al. 2020

DMSO

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“…The best previous method for EFV quantification, known to the authors, report a correlation of 0.97 and a 95% confidence interval between −18.43 and 14.91 ml measured on 50 CT images of the heart. 7 By using our proposed method on CTA images, we report a correlation of 0.99 and a 95% confidence interval between −14.02 and 11.21 ml. Both algorithms have approximately the same run-times.…”

Section: Discussionmentioning

confidence: 76%

“…The method recently presented by Ding et al 7 seems to be more accurate reporting a regression of 0.98 on their data set containing 50 CT volumes. Their work is an extension of the work done by Dey et al, 5 where the initial multiatlas segmentation is deformed by active contours driven by white lines (representing the pericardium) detected by a difference-of-Gaussians approach.…”

Section: Related Workmentioning

confidence: 94%

Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography

et al. 2016

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Recent findings indicate a strong correlation between the risk of future heart disease and the volume of adipose tissue inside of the pericardium. So far, large-scale studies have been hindered by the fact that manual delineation of the pericardium is extremely time-consuming and that existing methods for automatic delineation lack accuracy. An efficient and fully automatic approach to pericardium segmentation and epicardial fat volume (EFV) estimation is presented, based on a variant of multi-atlas segmentation for spatial initialization and a random forest classifier for accurate pericardium detection. Experimental validation on a set of 30 manually delineated computer tomography angiography volumes shows a significant improvement on state-of-the-art in terms of EFV estimation [mean absolute EFV difference: 3.8 ml (4.7%), Pearson correlation: 0.99] with run times suitable for large-scale studies (52 s). Further, the results compare favorably with interobserver variability measured on 10 volumes.

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“…Dey et al 6 and Ding et al 9,21 also used multiatlas-based algorithms for automated segmentation of the heart region and epicardial fat in CT images. With a sufficient number of atlas images that capture major variations in different patients, multiatlas-based methods can accurately segment the boundaries of the heart region without postrefining processing on cardiac CT, mainly due to the high-resolution and high-contrast characteristics of those images.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

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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 [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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Automated pericardium delineation and epicardial fat volume quantification from noncontrast CT

Abstract: The authors' novel automated method based on atlas-initialized active contours accurately and rapidly quantifies EFV from noncontrast CT.

Cited by 34 publications

References 37 publications

<p>Bone Morphogenetic Protein-2 and Osteopontin Gene Expression in Epicardial Adipose Tissue from Patients with Coronary Artery Disease Is Associated with the Presence of Calcified Atherosclerotic Plaques</p>

<p>Bone Morphogenetic Protein-2 and Osteopontin Gene Expression in Epicardial Adipose Tissue from Patients with Coronary Artery Disease Is Associated with the Presence of Calcified Atherosclerotic Plaques</p>

Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography

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

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