Fully automated calcium detection using optical coherence tomography

Optical Coherence Tomography (OCT) is a new invasive technology for performing high-resolution cross-sectional imaging of the coronary arteries. In OCT images only Calcified plaque (CA) components can be accurately depicted as light penetrates hard tissue. In this work we present an automated method for detecting CA in OCT images. The method is fully automated as no user intervention is needed and includes three steps. In the first step the region between the lumen and the maximum penetration depth of OCT from the lumen border is determined. In the second step the region is classified into 3 clusters using the K-means algorithm. CA is identified using the results of k-means. The method was validated using expeerts' annotations on 27 images. The sensitivity of the method is 83% with Positive predictive value (PVV) 74 %.

[1]  Raimund Erbel,et al.  Impact of moderate lesion calcium on mechanisms of coronary stenting as assessed with three-dimensional intravascular ultrasound in vivo. , 2003, The American journal of cardiology.

[2]  A. Rollins,et al.  Intracoronary optical coherence tomography: a comprehensive review clinical and research applications. , 2009, JACC. Cardiovascular interventions.

[3]  Akiko Maehara,et al.  Consensus standards for acquisition, measurement, and reporting of intravascular optical coherence tomography studies: a report from the International Working Group for Intravascular Optical Coherence Tomography Standardization and Validation. , 2012, Journal of the American College of Cardiology.

[4]  Eloisa Arbustini,et al.  Expert review document on methodology, terminology, and clinical applications of optical coherence tomography: physical principles, methodology of image acquisition, and clinical application for assessment of coronary arteries and atherosclerosis. , 2010, European heart journal.

[5]  Dimitris Koutsouris,et al.  Optical Coherence Tomography: Basic Principles of Image Acquisition , 2012 .

[6]  N. Bruining,et al.  Expert review document part 2: methodology, terminology and clinical applications of optical coherence tomography for the assessment of interventional procedures , 2012, European heart journal.

[7]  E. Halpern,et al.  Characterization of Human Atherosclerosis by Optical Coherence Tomography , 2002, Circulation.

[8]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[9]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[10]  Renu Virmani,et al.  Vulnerable plaque: the pathology of unstable coronary lesions. , 2002, Journal of interventional cardiology.

[11]  Dimitrios I. Fotiadis,et al.  Atherosclerotic plaque characterization in Optical Coherence Tomography images , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Gijs van Soest,et al.  Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging. , 2010, Journal of biomedical optics.

[13]  Chenyang Xu,et al.  Characterization of atherosclerosis plaques by measuring both backscattering and attenuation coefficients in optical coherence tomography. , 2008, Journal of biomedical optics.