Every year, 100s of thousands of patients in the US are treated with intravascular stents. Although the technology has advanced and drug eluting metal stents hinder restenosis, there remains significant room for improvement. Stent parameters include drug choice, bioresorbable versus metal, mechanical design, coatings to stimulate cell coverage, etc. To optimize designs, sensitive, in vivo assessments are needed for preclinical and clinical studies. Intravascular OCT (iOCT) alone provides the resolution and contrast s necessary for in vivo interrogation of vascular healing;stent deployment issues such as malposition;and assessment of stent strut tissue coverage. The Cardiovascular Imaging Core Laboratory at CWRU analyzes iOCT images as a service to numerous clinical and preclinical trials from around the world. An analyst takes many hours to analyze manually a single stent, greatly limiting the size and number of studies. Despite training and quality assurance measures, inter-analyst variability limits the ability to determine changes between stent types. We will develop highly automated software to greatly speed analysis, improve reproducibility, increase accuracy, etc. Careful evaluations/validations will be performed using our database of >1500 manually analyzed stents, and new phantom and pig studies. With the successful completion of this research and development, we will deliver well-validated, highly automated software, which will enable routine use of iOCT for sensitive evaluation of emerging stent technologies, thereby providing greatly improved treatments of cardiovascular disease. In addition, fast, robust software will contribute to clinical usage of iOCT for assessment of stent deployment and healing of a stented vessel.
We will develop methods for improved in vivo assessment of intravascular stents, the treatment of choice for 100s of thousands of patients, suffering from ischemic heart disease, in the US every year. Our methods will enable optimization of stent technologies for improved treatment of vascular disease.
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|Shalev, Ronny; Gargesha, Madhusudhana; Prabhu, David et al. (2016) Processing to determine optical parameters of atherosclerotic disease from phantom and clinical intravascular optical coherence tomography three-dimensional pullbacks. J Med Imaging (Bellingham) 3:024501|
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|Mehanna, Emile; Bezerra, Hiram G; Prabhu, David et al. (2013) Volumetric characterization of human coronary calcification by frequency-domain optical coherence tomography. Circ J 77:2334-40|
|Wang, Zhao; Chamie, Daniel; Bezerra, Hiram G et al. (2012) Volumetric quantification of fibrous caps using intravascular optical coherence tomography. Biomed Opt Express 3:1413-26|
|Lu, Hong; Gargesha, Madhusudhana; Wang, Zhao et al. (2012) Automatic stent detection in intravascular OCT images using bagged decision trees. Biomed Opt Express 3:2809-24|
|Mehanna, Emile Aziz; Attizzani, Guilherme Ferragut; Kyono, Hiroyuki et al. (2011) Assessment of coronary stent by optical coherence tomography, methodology and definitions. Int J Cardiovasc Imaging 27:259-69|
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