Optical coherence tomography (OCT) is a light-based intracoronary imaging modality that provides high-resolution cross-sectional images of the luminal and plaque morphology. Currently, the segmentation of OCT images and identification of the composition of plaque are mainly performed manually by expert observers. However, this process is laborious and time consuming and its accuracy relies on the expertise of the observer. To address these limitations, we present a methodology that is able to process the OCT data in a fully automated fashion. The proposed methodology is able to detect the lumen borders in the OCT frames, identify the plaque region, and detect four tissue types: calcium (CA), lipid tissue (LT), fibrous tissue (FT), and mixed tissue (MT). The efficiency of the developed methodology was evaluated using annotations from 27 OCT pullbacks acquired from 22 patients. High Pearson's correlation coefficients were obtained between the output of the developed methodology and the manual annotations (from 0.96 to 0.99), while no significant bias with good limits of agreement was shown in the Bland-Altman analysis. The overlapping areas ratio between experts' annotations and methodology in detecting CA, LT, FT, and MT was 0.81, 0.71, 0.87, and 0.81, respectively.

Intravascular ultrasound (IVUS) virtual histology (VH-IVUS) is a new technique, which provides automated plaque characterization in IVUS frames, using the ultrasound backscattered RF-signals. However, its computation can only be performed once per cardiac cycle (ECG-gated technique), which significantly decreases the number of characterized IVUS frames. Also atherosclerotic plaques in images that have been acquired by machines, which are not equipped with the VH software, cannot be characterized. To address these limitations, we have developed a plaque characterization technique that can be applied in grayscale IVUS images. Our semiautomated method is based on a three-step approach. In the first step, the plaque area [region of interest (ROI)] is detected semiautomatically. In the second step, a set of features is extracted for each pixel of the ROI and in the third step, a random forest classifier is used to classify these pixels into four classes: dense calcium, necrotic core, fibrotic tissue, and fibro-fatty tissue. In order to train and validate our method, we used 300 IVUS frames acquired from virtual histology examinations from ten patients. The overall accuracy of the proposed method was 85.65% suggesting that our approach is reliable and may be further investigated in the clinical and research arena.

The centreline methodology provides geometrically correct models and permits reliable ESS computation. The ability to utilise data acquired during routine coronary angiography and IVUS examination will facilitate clinical investigation of the role of local ESS patterns in the natural history of coronary atherosclerosis.

We present a novel and time-efficient method for intracoronary lumen detection which produces three-dimensional (3D) coronary arteries using Optical Coherence Tomographic (OCT) images. OCT images are acquired for multiple patients and longitudinal cross-section (LOCS) images are reconstructed using different acquisition angles. The lumen contours for each LOCS image are extracted and translated to 2D cross-sectional images. Using two angiographic projections the centerline of the coronary vessel is reconstructed in 3D and the detected 2D contours are transformed to 3D and placed perpendicular to the centerline. To validate the proposed method, 613 manual annotations from medical experts were used as gold standard. The 2D detected contours were compared to the annotated contours and the 3D reconstructed models produced using the detected contours were compared to the models produced by the annotated contours. Wall shear stress (WSS), as dominant hemodynamics factor, was calculated using computational fluid dynamics and 844 consecutive 2-mm segments of the 3D models were extracted and compared to each other. High Pearson’s correlation coefficients were obtained for the lumen area (r=0.98) and local WSS (r=0.97) measurements, while no significant bias with good limits of agreement was shown in the Bland-Altman analysis. The overlapping and non-overlapping areas ratio between experts’ annotations and presented method was 0.92 and 0.14, respectively. The proposed computer-aided lumen extraction and 3D vessel reconstruction method is fast, accurate and likely to assist in a number of research and clinical applications.