Abstract: A patient's physiological parameters, ascertained using intravascular ultrasound (IVUS) in combination with virtual histology (VH), are evaluated to predict whether the patient's physiology has an increased risk of producing a Clinical Event or a Silent Clinical Event. In one embodiment, one or more physiological parameters, such as Plaque Burden, Minimum Lumen Area, and whether or multiple VH-TCFAs are present, are compared to target values and/or ranges. A concurrence of the values for the physiological parameters to the target ranges indicates that a patient's physiology has an increased risk of producing a Clinical Event or a Silent Clinical Event.

Abstract: A system and method are disclosed for automatically classifying plaque lesions. A plaque classification application applies a plaque classification criterion to at least one graphical image, comprising a map of spectrally-analyzed characterized tissue of a vessel cross-section, to render an overall plaque classification for the slice or set of slices, covering a 3D volume. The plaque classification is based upon the amount and location of each characterized tissue type (e.g., necrotic core—NC). In an exemplary embodiment the set of potential plaque classifications, not to be confused with characterized tissue types—from which the plaque classifications are derived—include, for example: adaptive intimal thickening (AIT), pathological intimal thickening (PIT), fibroatheroma (FA), thin-cap fibroatheroma (TCFA), and fibro-calcific (FC).

Abstract: A method and system are disclosed for creating, in a coordinated manner, graphical images of a body including vascular features from a combination of image data sources. The method includes initially creating an angiographic image of a vessel segment. The angiographic image is, for example, either a two or three dimensional image representation. Next, a vessel image data set is acquired that is distinct from the angiographic image data. The vessel image data set comprises information acquired at a series of positions along the vessel segment. An example of such vessel image data is a set of intravascular ultrasound frames corresponding to circumferential cross-section slices taken at various positions along the vessel segment. The angiographic image and the vessel image data set are correlated by comparing a characteristic rendered independently from both the angiographic image and the vessel image data at positions along the vessel segment.