Automated Quantification of Epicardial Adipose Tissue Using CT Angiography: Evaluation of a Prototype Software

Spearman, James V.; Meinel, Felix G.; Schoepf, U. Joseph; Apfaltrer, Paul; Silverman, Justin R.; Krazinski, Aleksander W.; Canstein, Christian; Cecco, Carlo N. De; Costello, Philip; Geyer, Lucas L.

doi:10.1007/s00330-013-3052-2

Cited by 29 publications

(39 citation statements)

References 30 publications

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“…Therefore, a single, linear measurement does not provide information on the distribution, overall volume, or change in CAT volume over time. Many have worked to address the limitation using cardiac CT [10][11][12][13][14] , but MRI does not require radiation and pulse sequences can be designed to target adipose tissue specifically [19,20,41,42] .…”

Section: Discussionmentioning

confidence: 99%

“…Cardiac CT has proven useful in quantifying CAT [8][9][10][11][12][13][14] , but is associated with the delivery of a large dose of radiation to patients. With increasing utilization of CT, the annual per-capita collective dose has increased by 600% since 2006 [15] .…”

Section: Introductionmentioning

confidence: 99%

“…The three-dimensional (3D) data sets created by MRI are rich with information, but analyzing the images to identify and quantify the CAT is cumbersome and time-consuming and has become a substantial bottleneck in studies quantifying adipose tissue, particularly where multiple scans are acquired from different time points in longitudinal studies [16] . Most of the work to-date on automated image analysis has been based on cardiac CT imaging [12,14,18] . Little work exists in quantifying CAT from cardiac MRI.…”

Section: Introductionmentioning

confidence: 99%

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Automated segmentation of cardiac adipose tissue in Dixon magnetic resonance images

Klingensmith

Elliott

Fernández-del-Valle

et al. 2017

JBGC

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Objective: Increasing evidence suggests a strong link between excess cardiac adipose tissue (CAT) and the risk of a cardiovascular event. Multi-echo Dixon magnetic resonance imaging (MRI), providing fat-only and water-only images, is a useful tool for quantification but requires the segmentation of CAT from a large number of images. The intent of this study was to evaluate an automated technique for CAT segmentation from Dixon MRI by comparing the contours identified by the automated algorithm to those manually traced by an observer.Methods: An automated segmentation algorithm, based on optimal thresholds and custom morphological processing, was applied to the registered fat-only and water-only images to identify CAT in the volume scans. CAT contours in 446 images, from 10 MRI scans, were selected for validation analysis. Cross-sectional area (CSA) and volume were computed and compared using Bland-Altman analysis. In addition, Hausdorff distance and Dice Similarity Coefficient (DSC) were used for assessment.Results: Linear regression analysis yielded correlation of R 2 = 0.381 for CSA and R 2 = 0.879 for volume. When compared to the observer, the computer algorithm under-estimated CSA by 27.5 ± 40.0% and volume by 26.4 ± 10.4%. The average bidirectional Hausdorff distance was 26.2 ± 16.0 mm while the average unidirectional Hausdorff distances were 24.5 ± 15.7 mm and 12.4 ± 11.7 mm. The average DSC was 0.561 ± 0.100. The time required for manual tracing was 15.84 ± 3.73 min and the time required for the computer algorithm was 2.81 ± 0.12 min. Conclusions:This study provided a technique, faster and less tedious than manual tracing (p < 0.00001), for quantification of CAT in Dixon MRI data, demonstrating feasibility of this approach for cardiac risk stratification.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated segmentation of cardiac adipose tissue in Dixon magnetic resonance images

Klingensmith

Elliott

Fernández-del-Valle

et al. 2017

JBGC

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“…"Dixon" technique sequences create a registered set of in-phase (IP) and out-of-phase (OOP) image volumes and can be used to highlight adipose tissue by producing fat-only and water-only images [13] . The three-dimensional (3D) data sets created by MRI are rich with information, but most of the work to date has focused on quantifying overall volume and has not assessed the distribution of the CAT [9,11,14] . In addition, automated and semi-automated algorithms have been developed to assess CAT volume from cardiac MRI, aiding in the cumbersome process of analyzing the large number of images involved with the 3D scans [15,16] .…”

mentioning

confidence: 99%

“…Both cardiac CT and cardiac magnetic resonance imaging (MRI) have proven useful in quantifying CAT [5][6][7][8][9][10][11] . MRI has emerged as a highly effective imaging modality for identifying whole body fat and ectopic fat depots [12] .…”

mentioning

confidence: 99%

Three-dimensional modeling and assessment of cardiac adipose tissue distribution.

Klingensmith

Sop

Naz

et al. 2018

JBGC

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Objective: The layer of fat that accumulates around the heart, called cardiac adipose tissue (CAT), can influence the development of coronary disease and is indicative of cardiovascular risk. While volumetric assessment of magnetic resonance imaging (MRI) can quantify CAT, volume alone gives no information about its distribution across the myocardial surface, which may be an important factor in risk assessment. In this study, a three-dimensional (3D) modeling technique is developed and used to quantify the distribution of the CAT across the surface of the heart. Methods: Dixon MRI scans, which produce a registered 3D set of fat-only and water-only images, were acquired in 10 subjects for a study on exercise intervention. A previously developed segmentation algorithm was used to identify the heart and CAT. Extracted contours were used to build 3D models. Procrustes analysis was used to register the heart models and an iterative closest point algorithm was used to register and align the CAT models for calculation of CAT thickness. Rays were cast in directions specified by a spherical parameterization of elevation and azimuthal angles, and intersections of the ray with the CAT surface were used to calculate the thickness at each location. To evaluate the effects of the spherical parameterization on the thickness estimates, a set of synthetic models were created with increasing major-to-minor axis ratios.Results: Based on the experimental data from the synthetic models, the average error in CAT thickness ranged from 1.25% to 17.3% for increasing major-to-minor axis ratio. Conclusions:A process was developed, based on Dixon MRI data, to provide 3D models of the myocardial surface and the cardiac fat. The models can be used in future segmentation algorithm development and for studies on changes in cardiac fat as a result of various interventions.

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Role of Epicardial Adipose Tissue in Health and Disease: A Matter of Fat?

Gaborit

Sengenès

Ancel

et al. 2017

Comprehensive Physiology

122

109

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Epicardial adipose tissue (EAT) is a small but very biologically active ectopic fat depot that surrounds the heart. Given its rapid metabolism, thermogenic capacity, unique transcriptome, secretory profile, and simply measurability, epicardial fat has drawn increasing attention among researchers attempting to elucidate its putative role in health and cardiovascular diseases. The cellular crosstalk between epicardial adipocytes and cells of the vascular wall or myocytes is high and suggests a local role for this tissue. The balance between protective and proinflammatory/profibrotic cytokines, chemokines, and adipokines released by EAT seem to be a key element in atherogenesis and could represent a future therapeutic target. EAT amount has been found to predict clinical coronary outcomes. EAT can also modulate cardiac structure and function. Its amount has been associated with atrial fibrillation, coronary artery disease, and sleep apnea syndrome. Conversely, a beiging fat profile of EAT has been identified. In this review, we describe the current state of knowledge regarding the anatomy, physiology and pathophysiological role of EAT, and the factors more globally leading to ectopic fat development. We will also highlight the most recent findings on the origin of this ectopic tissue, and its association with cardiac diseases. © 2017 American Physiological Society. Compr Physiol 7:1051-1082, 2017.

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Automated Quantification of Epicardial Adipose Tissue Using CT Angiography: Evaluation of a Prototype Software

Cited by 29 publications

References 30 publications

Automated segmentation of cardiac adipose tissue in Dixon magnetic resonance images

Automated segmentation of cardiac adipose tissue in Dixon magnetic resonance images

Three-dimensional modeling and assessment of cardiac adipose tissue distribution.

Role of Epicardial Adipose Tissue in Health and Disease: A Matter of Fat?

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