Abstract: Embodiments discussed herein facilitate classification of vascular plaque. One example embodiment can: access vascular imaging data comprising one or more slices, wherein each slice comprises a plurality of A-lines of that slice; for each A-line of the plurality of A-lines of each slice of the one or more slices: extract one or more features for that A-line, wherein the one or more features for that A-line comprise at least one of: one or more features extracted from that A-line, one or more features extracted from the slice comprising that A-line, or one or more features extracted from the vascular imaging data; provide the one or more features for that A-line to at least one classifier; and generate a classification of that A-line via the at least one classifier, wherein the classification of that A-line is one of fibrocalcific, fibrolipidic, or other.

Abstract: Embodiments discussed herein facilitate classification of vascular plaque. One example embodiment can: access vascular imaging data comprising one or more slices, wherein each slice comprises a plurality of A-lines of that slice; for each A-line of the plurality of A-lines of each slice of the one or more slices: extract one or more features for that A-line, wherein the one or more features for that A-line comprise at least one of: one or more features extracted from that A-line, one or more features extracted from the slice comprising that A-line, or one or more features extracted from the vascular imaging data; provide the one or more features for that A-line to at least one classifier; and generate a classification of that A-line via the at least one classifier, wherein the classification of that A-line is one of fibrocalcific, fibrolipidic, or other.

Abstract: This disclosure provides systems and methods to automatically classify stent struts as covered or uncovered and to measure the thickness of tissue coverage. As one example, the method includes storing three-dimensional image data acquired intravascularly via an optical coherence tomography (OCT) apparatus and detecting struts based on analysis of the image data. Image data corresponding to each of the detected struts is further analyzed automatically to compute an indication of tissue coverage for the stent.

Abstract: A method includes storing three-dimensional image data acquired intravascularly via an optical coherence tomography (OCT) apparatus. The image data is analyzed to compute a probability estimate of stent presence at support positions appearing in an A-line. Stent strut locations are located in three-dimensional space based on the computed probability estimate of stent presence.

Abstract: This disclosure provides systems and methods to automatically classify stent struts as covered or uncovered and to measure the thickness of tissue coverage. As one example, the method includes storing three-dimensional image data acquired intravascularly via an optical coherence tomography (OCT) apparatus and detecting struts based on analysis of the image data. Image data corresponding to each of the detected struts is further analyzed automatically to compute an indication of tissue coverage for the stent.

Abstract: A method includes storing three-dimensional image data acquired intravascularly via an optical coherence tomography (OCT) apparatus. The image data is analyzed to compute a probability estimate of stent presence at support positions appearing in an A-line. Stent strut locations are located in three-dimensional space based on the computed probability estimate of stent presence.