Top

Published in:

01-02-2014 | Cardiac

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

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

Published in: European Radiology | Issue 2/2014

Abstract

Objectives

This study evaluated the performance of a novel automated software tool for epicardial fat volume (EFV) quantification compared to a standard manual technique at coronary CT angiography (cCTA).

Methods

cCTA data sets of 70 patients (58.6 ± 12.9 years, 33 men) were retrospectively analysed using two different post-processing software applications. Observer 1 performed a manual single-plane pericardial border definition and EFV_M segmentation (manual approach). Two observers used a software program with fully automated 3D pericardial border definition and EFV_A calculation (automated approach). EFV and time required for measuring EFV (including software processing time and manual optimization time) for each method were recorded. Intraobserver and interobserver reliability was assessed on the prototype software measurements. T test, Spearman’s rho, and Bland–Altman plots were used for statistical analysis.

Results

The final EFV_A (with manual border optimization) was strongly correlated with the manual axial segmentation measurement (60.9 ± 33.2 mL vs. 65.8 ± 37.0 mL, rho = 0.970, P < 0.001). A mean of 3.9 ± 1.9 manual border edits were performed to optimize the automated process. The software prototype required significantly less time to perform the measurements (135.6 ± 24.6 s vs. 314.3 ± 76.3 s, P < 0.001) and showed high reliability (ICC > 0.9).

Conclusions

Automated EFV_A quantification is an accurate and time-saving method for quantification of EFV compared to established manual axial segmentation methods.

Key Points

• Manual epicardial fat volume quantification correlates with risk factors but is time-consuming.

• The novel software prototype automates measurement of epicardial fat volume with good accuracy.

• This novel approach is less time-consuming and could be incorporated into clinical workflow.

Bastarrika G, Broncano J, Schoepf UJ et al (2010) Relationship between coronary artery disease and epicardial adipose tissue quantification at cardiac CT: comparison between automatic volumetric measurement and manual bidimensional estimation. Acad Radiol 17:727–734PubMedCrossRef

Rosito GA, Massaro JM, Hoffmann U et al (2008) Pericardial fat, visceral abdominal fat, cardiovascular disease risk factors, and vascular calcification in a community-based sample: the Framingham Heart Study. Circulation 117:605–613PubMedCrossRef

Taguchi R, Takasu J, Itani Y et al (2001) Pericardial fat accumulation in men as a risk factor for coronary artery disease. Atherosclerosis 157:203–209PubMedCrossRef

Schlett CL, Ferencik M, Kriegel MF et al (2012) Association of pericardial fat and coronary high-risk lesions as determined by cardiac CT. Atherosclerosis 222:129–134PubMedCentralPubMedCrossRef

Mazurek T, Zhang L, Zalewski A et al (2003) Human epicardial adipose tissue is a source of inflammatory mediators. Circulation 108:2460–2466PubMedCrossRef

Sacks HS, Fain JN (2007) Human epicardial adipose tissue: a review. Am Heart J 153:907–917PubMedCrossRef

Apfaltrer P, Schoepf UJ, Vliegenthart R et al (2011) Coronary computed tomography–present status and future directions. Int J Clin Pract Suppl. doi:10.1111/j.1742-1241.2011.02784.x:3-13 PubMed

Gruettner J, Fink C, Walter T et al (2013) Coronary computed tomography and triple rule out CT in patients with acute chest pain and an intermediate cardiac risk profile. Part 1: impact on patient management. Eur J Radiol 82:100–105PubMedCrossRef

Moscariello A, Vliegenthart R, Schoepf UJ et al (2012) Coronary CT angiography versus conventional cardiac angiography for therapeutic decision making in patients with high likelihood of coronary artery disease. Radiology 265:385–392PubMedCrossRef

10.

Ebersberger U, Eilot D, Goldenberg R et al (2013) Fully automated derivation of coronary artery calcium scores and cardiovascular risk assessment from contrast medium-enhanced coronary CT angiography studies. Eur Radiol 23:650–657PubMedCrossRef

11.

Nichols JH, Samy B, Nasir K et al (2008) Volumetric measurement of pericardial adipose tissue from contrast-enhanced coronary computed tomography angiography: a reproducibility study. J Cardiovasc Comput Tomogr 2:288–295PubMedCentralPubMedCrossRef

12.

Wheeler GL, Shi R, Beck SR et al (2005) Pericardial and visceral adipose tissues measured volumetrically with computed tomography are highly associated in type 2 diabetic families. Invest Radiol 40:97–101PubMedCrossRef

13.

Park MJ, Jung JI, Oh YS, Youn HJ (2010) Assessment of epicardial fat volume with threshold-based 3-dimensional segmentation in CT: comparison with the 2-dimensional short axis-based method. Korean Circ J 40:328–333PubMedCentralPubMedCrossRef

14.

Iacobellis G, Bianco AC (2011) Epicardial adipose tissue: emerging physiological, pathophysiological and clinical features. Trends Endocrinol Metab 22:450–457PubMedCrossRef

15.

Iacobellis G, Corradi D, Sharma AM (2005) Epicardial adipose tissue: anatomic, biomolecular and clinical relationships with the heart. Nat Clin Pract Cardiovasc Med 2:536–543PubMedCrossRef

16.

Iacobellis G, Ribaudo MC, Assael F et al (2003) Echocardiographic epicardial adipose tissue is related to anthropometric and clinical parameters of metabolic syndrome: a new indicator of cardiovascular risk. J Clin Endocrinol Metab 88:5163–5168PubMedCrossRef

17.

Sicari R, Sironi AM, Petz R et al (2011) Pericardial rather than epicardial fat is a cardiometabolic risk marker: an MRI vs echo study. J Am Soc Echocardiogr 24:1156–1162PubMedCrossRef

18.

Marwan M, Achenbach S (2013) Quantification of epicardial fat by computed tomography: why, when and how? J Cardiovasc Comput Tomogr 7:3–10PubMedCrossRef

19.

Dey D, Suzuki Y, Suzuki S et al (2008) Automated quantitation of pericardiac fat from noncontrast CT. Invest Radiol 43:145–153PubMedCrossRef

20.

Xu Y, Cheng X, Hong K, Huang C, Wan L (2012) How to interpret epicardial adipose tissue as a cause of coronary artery disease: a meta-analysis. Coron Artery Dis 23:227–233PubMedCrossRef

21.

Greif M, Becker A, von Ziegler F et al (2009) Pericardial adipose tissue determined by dual source CT is a risk factor for coronary atherosclerosis. Arterioscler Thromb Vasc Biol 29:781–786PubMedCrossRef

22.

Tamarappoo B, Dey D, Shmilovich H et al (2010) Increased pericardial fat volume measured from noncontrast CT predicts myocardial ischemia by SPECT. JACC Cardiovasc Imaging 3:1104–1112PubMedCentralPubMedCrossRef

23.

Mahabadi AA, Berg MH, Lehmann N et al (2013) Association of epicardial fat with cardiovascular risk factors and incident myocardial infarction in the general population: the Heinz Nixdorf Recall Study. J Am Coll Cardiol 61:1388–1395PubMedCrossRef

24.

Shimabukuro M, Hirata Y, Tabata M et al (2013) Epicardial adipose tissue volume and adipocytokine imbalance are strongly linked to human coronary atherosclerosis. Arterioscler Thromb Vasc Biol 33:1077–1084PubMedCrossRef

25.

Cheng VY, Dey D, Tamarappoo B et al (2010) Pericardial fat burden on ECG-gated noncontrast CT in asymptomatic patients who subsequently experience adverse cardiovascular events. JACC Cardiovasc Imaging 3:352–360PubMedCentralPubMedCrossRef

26.

Nakanishi R, Rajani R, Cheng VY et al (2011) Increase in epicardial fat volume is associated with greater coronary artery calcification progression in subjects at intermediate risk by coronary calcium score: a serial study using non-contrast cardiac CT. Atherosclerosis 218:363–368PubMedCrossRef

27.

Nakazato R, Dey D, Cheng VY et al (2012) Epicardial fat volume and concurrent presence of both myocardial ischemia and obstructive coronary artery disease. Atherosclerosis 221:422–426PubMedCrossRef

28.

Achenbach S, Boehmer K, Pflederer T et al (2010) Influence of slice thickness and reconstruction kernel on the computed tomographic attenuation of coronary atherosclerotic plaque. J Cardiovasc Comput Tomogr 4:110–115PubMedCrossRef

29.

Birnbaum BA, Hindman N, Lee J, Babb JS (2007) Multi-detector row CT attenuation measurements: assessment of intra- and interscanner variability with an anthropomorphic body CT phantom. Radiology 242:109–119PubMedCrossRef

30.

Baker AR, Silva NF, Quinn DW et al (2006) Human epicardial adipose tissue expresses a pathogenic profile of adipocytokines in patients with cardiovascular disease. Cardiovasc Diabetol 5:1PubMedCentralPubMedCrossRef

31.

Cheng KH, Chu CS, Lee KT et al (2008) Adipocytokines and proinflammatory mediators from abdominal and epicardial adipose tissue in patients with coronary artery disease. Int J Obes (Lond) 32:268–274CrossRef

Title: Automated Quantification of Epicardial Adipose Tissue Using CT Angiography: Evaluation of a Prototype Software
Authors: James V. Spearman
Felix G. Meinel
U. Joseph Schoepf
Paul Apfaltrer
Justin R. Silverman
Aleksander W. Krazinski
Christian Canstein
Carlo Nicola De Cecco
Philip Costello
Lucas L. Geyer
Publication date: 01-02-2014
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 2/2014
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-013-3052-2

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Please log in to get access to this content

Other articles of this Issue 2/2014

Radiation dose reduction in cerebral CT perfusion imaging using iterative reconstruction

Is imaging the future of axillary staging in breast cancer?

Adult exposures from MDCT including multiphase studies: first Italian nationwide survey

Accuracy of dual-energy computed tomography for the measurement of iodine concentration using cardiac CT protocols: validation in a phantom model

Clinical validation of semi-automated software for volumetric and dynamic contrast enhancement analysis of soft tissue venous malformations on Magnetic Resonance Imaging examination

Low-tube-voltage (80 kVp) CT aortography using 320-row volume CT with adaptive iterative reconstruction: lower contrast medium and radiation dose