Kawasaki disease, a childhood pathology, is marked by the potential for coronary artery complications, which can lead to the dilation or inflammation of the blood vessel wall if left untreated. Intravascular Optical coherence tomography (IVOCT) was introduced for intravascular imaging of coronary arteries to provide valuable navigation guidance information to cardiologists. It requires a skilled operator, and the acquisition protocol is complex. The goal of this study is to present a framework to reproduce patient specific coronary OCT phantoms using polyvinyl alcohol cryogel (PVA-c), which can be used for training cardiologists and for better understanding of the OCT image formation process. This innovative approach enables us to produce phantoms with both mechanical and optical properties very similar to human tissue. To produce these phantoms, we design and print in 3D modular cylindrical molds from real OCT arterial images. A mixture of PVA is poured into the molds and submitted to three thaw and freeze cycles to create soft tissue that represent coronary arteries affected by Kawasaki disease. Once the phantoms have been created, OCT pull-back sequences are acquired and compared to the original images. We acknowledged that our PVA-c phantoms reproduces morphological shape and visual appearance on OCT very similar to human tissue. This holds true even when applied to extremely small morphologies.
Intravascular imaging modalities, such as Optical Coherence Tomography (OCT) allow nowadays improving diagnosis, treatment, follow-up, and even prevention of coronary artery disease in the adult. OCT has been recently used in children following Kawasaki disease (KD), the most prevalent acquired coronary artery disease during childhood with devastating complications. The assessment of coronary artery layers with OCT and early detection of coronary sequelae secondary to KD is a promising tool for preventing myocardial infarction in this population. More importantly, OCT is promising for tissue quantification of the inner vessel wall, including neo intima luminal myofibroblast proliferation, calcification, and fibrous scar deposits. The goal of this study is to classify the coronary artery layers of OCT imaging obtained from a series of KD patients. Our approach is focused on developing a robust Random Forest classifier built on the idea of randomly selecting a subset of features at each node and based on second- and higher-order statistical texture analysis which estimates the gray-level spatial distribution of images by specifying the local features of each pixel and extracting the statistics from their distribution. The average classification accuracy for intima and media are 76.36% and 73.72% respectively. Random forest classifier with texture analysis promises for classification of coronary artery tissue.
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