Abstract: There is described a system for determining a coronal artery tissue type. The system generally has an optical coherence tomography (OCT) imaging system being configured for acquiring an OCT image of coronal artery tissue; and a controller configured to perform the steps of: using trained feature extraction engines, extracting corresponding feature vectors comprising a plurality of features in at least a region of interest of the OCT image; using trained classification engines, determining corresponding preliminary coronal artery tissue types associated to the region of interest of the OCT image based on corresponding ones of the plurality of feature vectors; and using a majority voting engine, majority voting an output coronal artery tissue type associated to the region of interest of the OCT image based on the previously determined preliminary coronal artery tissue types.

Abstract: There is described a system for determining an atherosclerotic pathological tissue type of a coronary artery, the system comprising: an optical coherence tomography (OCT) imaging system being configured for acquiring an OCT image of tissue within said coronary artery; and a controller configured for: using a trained fully convolutional engine stored on said memory and having a plurality of convolutional layers with respective dilation rates different than unity, extracting pathological tissues regardless their type in at least a region of interest of said OCT image; using a trained auto-encoder classification engine stored on said memory and having a layer characterized with a sparsity regularization parameter, determining an atherosclerotic pathological tissue type associated to said region of interest of said OCT image based on said extracted pathological tissues; and outputting said atherosclerotic pathological tissue type of said coronary artery.

Abstract: There is described a system for determining a coronal artery tissue type. The system generally has an optical coherence tomography (OCT) imaging system being configured for acquiring an OCT image of coronal artery tissue; and a controller configured to perform the steps of: using trained feature extraction engines, extracting corresponding feature vectors comprising a plurality of features in at least a region of interest of the OCT image; using trained classification engines, determining corresponding preliminary coronal artery tissue types associated to the region of interest of the OCT image based on corresponding ones of the plurality of feature vectors; and using a majority voting engine, majority voting an output coronal artery tissue type associated to the region of interest of the OCT image based on the previously determined preliminary coronal artery tissue types.

Abstract: A method for three-dimensional reconstruction of a branched object from a rotational sequence of images of the branched object includes segmenting the branched object from each image of the sequence, extracting centerlines of the branched object, performing symbolic reconstruction via a stereo correspondence matching between the centerlines from different views of the sequence of images using a graph cut-based optimization, and creating a three-dimensional tomographic reconstruction of the branched object compensated for motion of the branched object between the images of the sequence.

Abstract: A method for displaying real-time imagery of coronary arteries including a chronic total occlusion (CTO) includes acquiring three-dimensional image data of coronary arteries using a three-dimensional medical imaging device, wherein the three-dimensional image data includes imagery of the CTO. A radiocontrast agent is administered to a patient. Real-time image data of the coronary arteries are acquired using one or more fluoroscopes. The real-time image data does not include imagery of the CTO and down-stream vessel structure. The three-dimensional image data is co-registered with the real-time image data using an image processing device within a vicinity of the CTO. The co-registered image data are displayed in real-time using a display device to accurately illustrate the location of the CTO within the context of the real-time image data.

Abstract: A method for three-dimensional reconstruction of a branched object from a rotational sequence of images of the branched object includes segmenting the branched object from each image of the sequence, extracting centerlines of the branched object, performing symbolic reconstruction via a stereo correspondence matching between the centerlines from different views of the sequence of images using a graph cut-based optimization, and creating a three-dimensional tomographic reconstruction of the branched object compensated for motion of the branched object between the images of the sequence.