Journal article  Open Access

Quantification of epicardial fat by cardiac CT imaging

Coppini G., Favilla R., Marraccini P., Moroni D., Pieri G.

Image segmentation  cardiac CT.  Level set methods  epicardial fat  Article  Cardiac CT  Epicardial fat  level set methods 

The aim of this work is to introduce and design image processing methods for the quantitative analysis of epicardial fat by using cardiac CT imaging. Indeed, epicardial fat has recently been shown to correlate with cardiovascular disease, cardiovascular risk factors and metabolic syndrome. However, many concerns still remain about the methods for measuring epicardial fat, its regional distribution on the myocardium and the accuracy and reproducibility of the measurements. In this paper, a method is proposed for the analysis of single-frame 3D images obtained by the standard acquisition protocol used for coronary calcium scoring. In the design of the method, much attention has been payed to the minimization of user intervention and to reproducibility issues. In particular, the proposed method features a two step segmentation algorithm suitable for the analysis of epicardial fat. In the first step of the algorithm, an analysis of epicardial fat intensity distribution is carried out in order to define suitable thresholds for a first rough segmentation. In the second step, a variational formulation of level set methods - including a specially-designed region homogeneity energy based on Gaussian mixture models- is used to recover spatial coherence and smoothness of fat depots. Experimental results show that the introduced method may be efficiently used for the quantification of epicardial fat.

Source: The Open medical informatics journal 4 (2010): 126–135. doi:10.2174/1874431101004010126

Publisher: Bentham Science Publishers, Hilversum , Paesi Bassi

Sacks, HS, Fain, JN. Human epicardial adipose tissue: A review. Am Heart J. 2007; 153: 907-17
Rosito, GA, Massaro, JM, Hoffmann, U. Pericardial fat, visceral abdominal fat, cardiovascular disease risk factors, and vascular calcification in a community-based sample: the framingham heart study. Circulation. 2008; 117: 605-13
Wang, TD, Lee, WJ, Shih, FY. Relations of epicardial adipose tissue measured by multidetector computed tomography to components of the metabolic syndrome are region-specific and independent of anthropometric indexes and intraabdominal visceral fat. J Clin Endocrinol Metab. 2009; 94 (2): 662-9
Mazurek, T, Zhang, L, Zalewski, A. Human epicardial adipose tissue is a source of inflammatory mediators. Circulation. 2003; 108 (20): 2460-6
Baker, A, Silva, N, Quinn, D. Human epicardial adipose tissue expresses a pathogenic profile of adipocytokines in patients with cardiovascular disease. Cardiovasc Diabetol. 2006; 5: 1
Iacobellis, G, Assael, F, Ribaudo, MC. Epicardial fat from echocardiography: a new method for visceral adipose tissue prediction. Obesity. 2003; 11 (2): 304-10
Iacobellis, G. Echocardiographic epicardial fat: A new tool in the white coat pocket. Nutr Metab Cardiovasc Dis. 2008; 18 (8): 519-22
Chaowalit, N, Somers, VK, Pellikka, PA, Rihal, CS, Lopez-Jimenez, F. Subepicardial adipose tissue and the presence and severity of coronary artery disease. Atherosclerosis. 2006; 186 (2): 354-9
Abbara, S, Desai, JC, Cury, RC, Butler, J, Nieman, K, Reddy, V. Mapping epicardial fat with multi-detector computed tomography to facilitate percutaneous transepicardial arrhythmia ablation. Eur J Radiol. 2006; 57 (3): 417-22
Detrano, R, Guerci, AD, Carr, JJ. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med. 2008; 358 (13): 1336-45
Mahnken, AH, Mühlenbruch, G, Günter, GW, Wildberger, JE. Cardiac CT: coronary arteries and beyond. Eur Radiol. 2007; 17: 994-1008
Moroni, D, Perner, P, Salvetti, O. A general approach to shape characterisation for biomedical problems. Int J Signal Imaging Syst Eng. 2008; 1: 30-5
Yoshizumi, T, Nakamura, T, Yamane, M. Abdominal fat: standardized technique for measurement at CT. Radiology. 1999; 211 (1): 283-6
Pednekar, A, Bandekar, AN, Kakadiaris, IA, Naghavi, M. Automatic Segmentation of Abdominal Fat from CT Data. 2005: 308-15
Bandekar, AN, Kakadiaris, IA. Automated pericardial fat quantification in CT data. 2006: 932-6
Dey, D, Suzuki, Y, Suzuki, S. Automated quantitation of pericardiac fat from noncontrast CT. Invest Radiol. 2008; 43 (2 ): 45-53
Dey, D, Wong, ND, Tamarappoo, B. Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and metabolic syndrome. Atherosclerosis. 2009
Figueiredo, B, Barbosa, JG, Bettencourt, N, Tavares, JMRS. Semi-automatic quantification of the epicardial fat in CT images. 2009: 247-50
Leinhard, D, Johansson, A, Rydell, J. Quantitative abdominal fat estimation using MRI. 2008
Coppini, G, Favilla, R, Lami, E, Marraccini, P, Moroni, D, Salvetti, O. Regional epicardial fat measurement: computational methods for cardiac CT imaging. Trans Mass-Data Anal Images Signals. 2009; 1 (1): 101-10
Caselles, V, Kimmel, R, Sapiro, G. Geodesic active contours. Int J Comput Vis. 1997; 22: 61-79
Riklin-Raviv, T, Sochen, NA, Kiryati, N. Propagating Distributions for Segmentation of Brain Atlas. 2007: 1304-7
Li, C, Xu, C, Gui, C, Fox, MD. Level set evolution without re-initialization: a new variational formulation. 2005: 430-6
Dempster, AP, Laird, NM, Rubin, DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B Stat Methodol. 1977; 39 (1): 1-38


Back to previous page
BibTeX entry
	title = {Quantification of epicardial fat by cardiac CT imaging},
	author = {Coppini G. and Favilla R. and Marraccini P. and Moroni D. and Pieri G.},
	publisher = {Bentham Science Publishers, Hilversum , Paesi Bassi},
	doi = {10.2174/1874431101004010126},
	journal = {The Open medical informatics journal},
	volume = {4},
	pages = {126–135},
	year = {2010}