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Intravascular optical coherence tomography (IVOCT) is rapidly becoming the method of choice for the in vivo investigation of coronary artery disease. While IVOCT visualizes atherosclerotic plaques with a resolution

International audience; Optical coherence tomography (OCT) is an optical diagnostic modality that can acquire cross-sectional images of the microscopic structure of the esophagus, including Barrett's esophagus (BE) and associated dysplasia. We developed a swallowable tethered capsule OCT endomicroscopy (TCE) device that acquires high-resolution images of entire gastrointestinal (GI) tract luminal organs. This device has a potential to become a screening method that identifies patients with an abnormal esophagus that should be further referred for upper endoscopy. Currently, the characterization of the OCT-TCE esophageal wall data set is performed manually, which is time-consuming and inefficient. Additionally, since the capsule optics optimally focus light approximately 500 µm outside the capsule wall and the best quality images are obtained when the tissue is in full contact with the capsule, it is crucial to provide feedback for the operator about tissue contact during the imaging procedure. In this study, we developed a fully automated algorithm for the segmentation of in vivo OCT-TCE data sets and characterization of the esophageal wall. The algorithm provides a two-dimensional representation of both the contact map from the data collected in human clinical studies as well as a tissue map depicting areas of BE with or without dysplasia. Results suggest that these techniques can potentially improve the current TCE data acquisition procedure and provide an efficient characterization of the diseased esophageal wall.

While optical coherence tomography (OCT) has been shown to be capable of imaging coronary plaque microstructure, additional chemical/molecular information may be needed in order to determine which lesions are at risk of causing an acute coronary event. In this study, we used a recently developed imaging system and double-clad fiber (DCF) catheter capable of simultaneously acquiring both OCT and red excited near-infrared autofluorescence (NIRAF) images (excitation: 633 nm, emission: 680nm to 900nm). We found that NIRAF is elevated in lesions that contain necrotic core – a feature that is critical for vulnerable plaque diagnosis and that is not readily discriminated by OCT alone. We first utilized a DCF ball lens probe and a bench top setup to acquire en face NIRAF images of aortic plaques ex vivo (n = 20). In addition, we used the OCT-NIRAF system and fully assembled catheters to acquire multimodality images from human coronary arteries (n = 15) prosected from human cadaver hearts (n = 5). Comparison of these images with corresponding histology demonstrated that necrotic core plaques exhibited significantly higher NIRAF intensity than other plaque types. These results suggest that multimodality intracoronary OCT-NIRAF imaging technology may be used in the future to provide improved characterization of coronary artery disease in human patients.

Intravascular optical coherence tomography (IV-OCT) is an imaging modality that can be used for the assessment of intracoronary stents. Recent publications pointed to the fact that 3D visualizations have potential advantages compared to conventional 2D representations. However, 3D imaging still requires a time consuming manual procedure not suitable for on-line application during coronary interventions. We propose an algorithm for a rapid and fully automatic 3D visualization of IV-OCT pullbacks. IV-OCT images are first processed for the segmentation of the different structures. This also allows for automatic pullback calibration. Then, according to the segmentation results, different structures are depicted with different colors to visualize the vessel wall, the stent and the guide-wire in details. Final 3D rendering results are obtained through the use of a commercial 3D DICOM viewer. Manual analysis was used as ground-truth for the validation of the segmentation algorithms. A correlation value of 0.99 and good limits of agreement (Bland Altman statistics) were found over 250 images randomly extracted from 25 in vivo pullbacks. Moreover, 3D rendering was compared to angiography, pictures of deployed stents made available by the manufacturers and to conventional 2D imaging corroborating visualization results. Computational time for the visualization of an entire data sets resulted to be ~74 sec. The proposed method allows for the on-line use of 3D IV-OCT during percutaneous coronary interventions, potentially allowing treatments optimization.