This article [J. Biomed. Opt.. 17, , 076018 (2012)] was originally published online on 13 July 2012. After publication, the authors wished to add a statement of disclosure in the Acknowledgments section as follows:
Drs. Bouma and Tearney received nonclinical sponsored research from Terumo Corporation and Ninepoint Medical. Massachusetts General Hospital has patent licensing arrangements with Terumo Corporation and Ninepoint Medical. Drs. Bouma and Tearney have the right to receive royalty income from these licensing agreements. Drs. Bouma and Tearney also receive royalty income through MIT. Dr. Tearney receives consulting income from Samsung Advanced Institute of Technology and Merck.
This article was republished online on 18 September 2012.
Quantitative analysis of optical coherence tomography data can be strongly hampered by speckle. Here, we introduce a new method to reduce speckle, which leverages from Fourier-domain configurations and operates on individual axial scans. By subdividing the digitized spectrum into a number of distinct narrower windows, each with a different center frequency, several independent speckle patterns result. These can be averaged to yield a lower-resolution image with strongly reduced speckle. The full resolution image remains available for human interpretation; the low resolution version can be used for parametric imaging or quantitative analysis. We demonstrate this technique using intravascular optical frequency domain imaging data acquired in vivo.
Optical coherence tomography (OCT) is rapidly becoming the method of choice for assessing arterial wall pathology in vivo. Atherosclerotic plaques can be diagnosed with high accuracy, including measurement of the thickness of fibrous caps, enabling an assessment of the risk of rupture. While the OCT image presents morphological information in highly resolved detail, it relies on interpretation of the images by trained readers for the identification of vessel wall components and tissue type. We present a framework to enable systematic and automatic classification of atherosclerotic plaque constituents, based on the optical attenuation coefficient µt of the tissue. OCT images of 65 coronary artery segments in vitro, obtained from 14 vessels harvested at autopsy, are analyzed and correlated with histology. Vessel wall components can be distinguished based on their optical properties: necrotic core and macrophage infiltration exhibit strong attenuation, µt10 mm−1, while calcific and fibrous tissue have a lower µt2−5mm−1. The algorithm is successfully applied to OCT patient data, demonstrating that the analysis can be used in a clinical setting and assist diagnostics of vessel wall pathology.