Abstract: The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

Abstract: The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

Abstract: The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

Abstract: The present disclosure provides systems and methods for dynamically visualizing the delivery of a device within a vessel by correlating at least one first extraluminal image with second extraluminal images. The extraluminal images may be correlated based on motion features, without the use of other sensors or timestamps. The first extraluminal image may be a high dose contrast x-ray angiogram (“XA”) and the second extraluminal images may be low dose contrast XAs. The high dose contrast XA may be used to generate a vessel map. The low dose contrast XAs may be taken during the delivery of a device, such as a balloon, stent, probe, or the like. Correlating the high dose XA and low dose XA based on motion features allows for the vessel map to be overlaid on the low dose XA to provide the physician visualization of where the device is within the vessel tree in real time.

Abstract: The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

Abstract: Aspects of the disclosure relate to systems, methods, and algorithms to train a machine learning model or neural network to classify OCT images. The neural network or machine learning model can receive annotated OCT images indicating which portions of the OCT image are blocked and which are clear as well as a classification of the OCT image as clear or blocked. After training, the neural network can be used to classify one or more new OCT images. A user interface can be provided to output the results of the classification and summarize the analysis of the one or more OCT images.

Abstract: Aspects of the disclosure provide for methods, systems, and apparatuses, including computer-readable storage media, for lipid detection by identifying fibrotic caps in medical images of blood vessels. A method includes receiving one or more input images of a blood vessel and processing the one or more input images using a machine learning model trained to identify locations of fibrotic caps in blood vessels. The machine learning model is trained using a plurality of training images each annotated with locations of one or more fibrotic caps. A method includes identifying and characterizing fibrotic caps of lipid pools based on differences in radial signal intensities measured at different locations of an input image. A system can generate one or more output images having segments that are visually annotated representing predicted locations of fibrotic caps covering lipidic plaques.

Abstract: The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

Abstract: The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

Abstract: The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.