BackgroundCoronary artery aneurysms (CAA) are serious complications of Kawasaki disease (KD). Optical coherence tomography (OCT) is a high-resolution intracoronary imaging modality that characterizes coronary artery wall structure. The purpose of this work was to describe CAA wall sequelae after KD.Methods and ResultsKD patients scheduled for routine coronary angiography underwent OCT imaging between March 2013 and August 2014. Subjects’ clinical courses, echocardiography, and coronary angiography examinations were reviewed retrospectively. OCT was performed in 18 patients aged 12.4±5.5 years, 9.0±5.1 years following onset of KD. Of those, 14 patients (77.7%) had a history of CAA (7 with giant CAA and 7 with regressed CAA at time of OCT). Intracoronary nitroglycerin was given to all patients (88.4±45.5 μg/m2). Mean radiation dose was 10.9±5.2 mGy/kg. One patient suffered from a transitory uneventful vasospasm at the site of a regressed CAA; otherwise no major procedural complications occurred. The most frequent abnormality observed on OCT was intimal hyperplasia (15 patients, 83.3%) seen at both aneurysmal sites and angiographically normal segments amounting to 390.8±166.0 μm for affected segments compared to 61.7±17 μm for unaffected segments (P<0.001). Disappearance of the media, and presence of fibrosis, calcifications, macrophage accumulation, neovascularization, and white thrombi were seen in 72.2%, 77.8%, 27.8%, 44.4%, and 33.3% of patients.ConclusionsIn this study, OCT proved safe and insightful in the setting of KD, with the potential to add diagnostic value in the assessment of coronary abnormalities in KD. The depicted coronary structural changes correspond to histological findings previously described in KD.

Intravascular optical coherence tomography (IV‐OCT) is a light‐based imaging modality with high resolution, which employs near‐infrared light to provide tomographic intracoronary images. Morbidity caused by coronary heart disease is a substantial cause of acute coronary syndrome and sudden cardiac death. The most common intracoronay complications caused by coronary artery disease are intimal hyperplasia, calcification, fibrosis, neovascularization and macrophage accumulation, which require efficient prevention strategies. OCT can provide discriminative information of the intracoronary tissues, which can be used to train a robust fully automatic tissue characterization model based on deep learning. In this study, we aimed to design a diagnostic model of coronary artery lesions. Particularly, we trained a random forest using convolutional neural network features to distinguish between normal and diseased arterial wall structure. Then, based on the arterial wall structure, fully convolutional network is designed to extract the tissue layers in normal cases, and pathological tissues regardless of lesion type in pathological cases. Then, the type of the lesions can be characterized with high precision using our previous model. The results demonstrate the robustness of the model with the approximate overall accuracy up to 90%.

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Purpose: Coronary artery events are mainly associated with atherosclerosis in adult population, which is recognized as accumulation of plaques in arterial wall tissues. Optical Coherence Tomography (OCT) is a light-based imaging system used in cardiology to analyze intracoronary tissue layers and pathological formations including plaque accumulation. This state-of-the-art catheter-based imaging system provides intracoronary cross-sectional images with high resolution of 10-15 µm. But interpretation of the acquired images is operator dependent, which is not only very time-consuming but also highly error prone from one observer to another. An automatic and accurate coronary plaque tagging using OCT image post-processing can contribute to wide adoption of the OCT system and reducing the diagnostic error rate. Method: In this study, we propose a combination of spatial pyramid pooling module with dilated convolutions for semantic segmentation to extract atherosclerotic tissues regardless of their types and training a sparse auto-encoder to reconstruct the input features and enlarge the training data as well as plaque type characterization in OCT images. Results: The results demonstrate high precision of the proposed model with reduced computational complexity, which can be appropriate for real-time analysis of OCT images. At each step of the work, measured accuracy, sensitivity, specificity of more than 93% demonstrate high performance of the model. Conclusion:The main focus of this study is atherosclerotic tissue characterization using OCT imaging. This contributes to wide adoption of the OCT imaging system by providing clinicians with a fully automatic interpretation of various atherosclerotic tissues. Future studies will be focused on analyzing atherosclerotic vulnerable plaques, those coronary plaques which are prone to rupture.

The present study demonstrates an independent association between incomplete revascularization and decreased LVEF recovery in patients with left ventricular dysfunction undergoing TAVI for severe aortic stenosis.