Abstract
Coronary artery disease is the leading cause of death in the world. Intravascular optical coherence tomography (IVOCT) is rapidly becoming a promising imaging modality for characterization of atherosclerotic plaques and evaluation of coronary stenting. OCT has several unique advantages over alternative technologies, such as intravascular ultrasound (IVUS), due to its better resolution and contrast. For example, OCT is currently the only imaging modality that can measure the thickness of the fibrous cap of an atherosclerotic plaque in vivo. OCT also has the ability to accurately assess the coverage of individual stent struts by neointimal tissue over time. However, it is extremely time-consuming to analyze IVOCT images manually to derive quantitative diagnostic metrics. In this chapter, we introduce some computer-aided methods to automate the common IVOCT image analysis tasks.
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Acknowledgements
The authors thank Marco A. Costa, Hiram G. Bezerra, and all members of the Cardiovascular Imaging Core lab at the University Hospitals Case Medical Center (Cleveland OH); David L. Wilson, Michael Jenkins, David Prabbu, Hong Lu, and Madhusudhana Gargesha from the Department of Biomedical Engineering, and Soumya Ray from the Department of Electrical Engineering and Computer Science at Case Western Reserve University; Joseph M. Schmitt, Chenyang Xu, and other technical support from St. Jude Medical Inc (St. Paul, Minnesota). Some research presented here was supported in part by grants R01 HL114406, R21 HL108263 and R01 HL095717 from the National Institutes of Health and in part by the American Heart Association Predoctoral Fellowship (#11PRE7320034).
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Wang, Z., Wilson, D.L., Bezerra, H.G., Rollins, A.M. (2015). Image Processing in Intravascular OCT. In: Drexler, W., Fujimoto, J. (eds) Optical Coherence Tomography. Springer, Cham. https://doi.org/10.1007/978-3-319-06419-2_17
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