Restricted accessResearch articleFirst published online 2016-6-1
Investigated geometrical characteristics and image density of left ventricle of multi-detector computed tomography in early coronary artery disease patients
BACKGROUND: Coronary artery disease (CAD) remains the leading cause of death worldwide. Currently, cardiac multi-detector computed tomography (MDCT) is widely used to diagnose CAD. The purpose in this study is to identify informative and useful predictors from left ventricular (LV) in the early CAD patients using cardiac MDCT images.
MATERIALS AND METHODS: Study groups comprised 42 subjects who underwent a screening health examination, including laboratory testing and cardiac angiography by 64-slice MDCT angiography. Two geometrical characteristics and one image density were defined as shape, size and stiffness on MDCT image. The t-test, logistic regression, and receiver operating characteristic curve were applied to assess and identify the significant predictors. The Kappa statistics was used to exam the agreements with physician’s judgments (i.e., Golden of True, GOT).
RESULTS: The proposed three characteristics of LV MDCT images are important predictors and risk factors for the early CAD patients. These predictors present over 80% of AUC and higher odds ratio. The Kappa statistics was 0.68 for the combinations of shape and stiffness into logistic regression.
CONCLUSIONS: The shape, size and stiffness of the left ventricular on MDCT can be used to be the effective indicators in the early CAD patients. Besides, the combinations of shape and stiffness into logistic regression could provide substantial agreement with physician’s judgments.
GanY., XiaZ., XiongJ., ZhaoQ., HuY. and ZhangJ., Toward accurate tooth segmentation from computed tomography images using a hybrid level set model, Med Phys42(1) (2015), 14–27.
2.
WangL., ChenK.C., GaoY., ShiF., LiaoS., LiG., ShenS.G., YanJ., LeeP.K., ChowB., LiuN.X., XiaJ.J. and ShenD., Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization, Med Phys41(1) (2014), 043503.
3.
BildD.E., BluemkeD.A., BurkeG.L., DetranoR., Diez RouxA.V., FolsomA.R., GreenlandP., Jacob JrD.R., KronmalR., LiuK., NelsonJ.C., O’LearyD., SaadM.F., SheaS., SzkloM. and TracyR.P., Multi-ethnic study of atherosclerosis: Objectives and design, Am J Epidemiol156(9) (2002), 871–881.
4.
LuN.H., YehL.R., ChenT.B., HuangY.H., KuoC.M. and DingH.J., Analyzing coronary artery disease in patients with low CAC scores by 64-slice MDCT, Scientific World Journal2012 (2012), 907062.
5.
ZhangT.M., DongL., YuanY., HouJ.L., YangZ.G. and GuoY.K., Assessing heart function of patients with single left ventricular valvalar insufficiency leision using 64-slice multi-detector row CT angiography, Sichuan Da Xue Xue Bao Yi Xue Ban45(1) (2014), 89–92.
6.
BrodoefelH., BurgstahlerC., TsiflikasI., ReimannA., SchroederS., ClaussenC.D., HeuschmidM. and KoppA.F., Dual-source CT: Effect of heart rate, heart rate variability, and calcification on image quality and diagnostic accuracy, Radiology247(2) (2008), 346–355.
7.
MowattG., CookJ.A., HillisG.S., WalkerS., FraserC., JiaX. and WaughN., 64-Slice computed tomography angiography in the diagnosis and assessment of coronary artery disease: Systematic review and meta-analysis, Heart94(11) (2008), 1386–1393.
8.
EinsteinA.J., EllistonC.D., AraiA.E., ChenM.Y., MatherR., PearsonG.D., DelapazR.L., NickoloffE., DuttaA. and BrennerD.J., Radiation dose from single-heartbeat coronary CT angiography performed with a 320-detector row volume scanner, Radiology254(3) (2010), 698–706.
9.
AydinA., ÇubukR., AtasoyM.M., GürolT., SoyluÖ and DagdevirenB., The morphologic and functional features of LAD myocardial bridging at multi-detector computed tomography coronary angiography: Correlation with coronary artery disease, Turk Kardiyol Dern Ars43(1) (2015), 31–37.
10.
BayrakF., DegertekinM., ErogluE., GuneysuT., SevincD., GemiciG., MutluB. and AytaclarS., Evaluation of myocardial bridges with 64-slice computed tomography coronary angiography, Acta Cardiol64(3) (2009), 341–346.
11.
MukhopadhyayA., QianZ., BhandarkarS.M., LiuT., VorosS. and RinehartS., Morphological analysis of the left ventricular endocardial surface using a bag-of-features descriptor, IEEE J Biomed Health Inform19(4) (2015), 1483–1493.
12.
ComaniciuD., ZhouX.S. and KrishnanS., Robust real-time myocardial border tracking for echocardiography: An information fusion approach, IEEE Trans Med Imaging23(7) (2004), 849–860.
13.
WilsonK., GuiraudonG., JonesD. and PetersT.M., 4D shape registration for dynamic electrophysiological cardiac mapping,(Pt 2), Med Image Comput Comput Assist Interv9 (2006), 520–527.
14.
DavidG.L., Distinctive image features from scale-invariant keypoints, Int J Comput Vis60(2) (2004), 91–110.
15.
ComaniciuD., ZhouX.S. and KrishnanS., Robust real-time myocardial border tracking for echocardiography: An information fusion approach, IEEE Trans Med Imaging23(7) (2004), 849–60.
16.
QianZ., MetaxasD.N. and AxelL., Boosting and nonparametric based tracking of tagged MRI cardiac boundaries, Med Image Comput Comput Assist Interv9(Pt 1) (2006), 636–644.
