Abstract:
In part, the disclosure relates to methods, and systems suitable for evaluating image data from a patient on a real time or substantially real time basis using machine learning (ML) methods and systems. Systems and methods for improving diagnostic tools for end users such as cardiologists and imaging specialists using machine learning techniques applied to specific problems associated with intravascular images that have polar representations. Further, given the use of rotating probes to obtain image data for OCT, IVUS, and other imaging data, dealing with the two coordinate systems associated therewith creates challenges. The present disclosure addresses these and numerous other challenges relating to solving the problem of quickly imaging and diagnosis a patient such that stenting and other procedures may be applied during a single session in the cath lab.
Abstract:
Systems and methods are provided for the combination and display of both live and non-live patient images. The features described include collecting angiographic image data, and correlating angiographic image frames to time-varying data relating to the patient's heart cycle. This time-varying data may then be compared with the patient's live heart cycle data so that the collected angiographic image frames can be interlaced within a display of live fluoroscopic images of the patient.
Abstract:
The present disclosure provides systems and methods for automatically aligning intravascular data taken during a plurality of pullbacks in a target blood vessel. Extraluminal images may be taken to determine the location of the guide catheter in the target vessel. Two or more pullbacks may then be performed in the target vessel. The start and end point of each pullback may be determined. A distance between the end point of each pullback and the proximal tip, or junction point, of the guide catheter may be determined. A difference between the end point of each pullback and the junction point may be determined. The difference between the distances from the end of the pullback and the junction point may correspond to the distance to offset one of the representations of the pullbacks in order to automatically align the representation of the pullbacks.
Abstract:
The present disclosure provides systems and methods for determining a mean transit time of a bolus within the blood vessel by passing the bolus through the blood vessel while an intravascular imaging probe is held stationary. The probe may collect a plurality of image frames as the bolus passes the probe. The cross-sectional area of the bolus within the images frames may be determined by segmenting each image frame by thresholding, creating a vessel mask, and creating a contrast mask by applying an element-wise AND operator to the thresholded image and the vessel mask. The cross-sectional area of the bolus for the image frames may be plotted on an area dilution curve. Various fits may be applied to and various points may be identified on the area dilution curve. The various fits and points may be used to determine the mean transit time.
Abstract:
In part, the disclosure relates to methods, and systems suitable for evaluating image data from a patient on a real time or substantially real time basis using machine learning (ML) methods and systems. Systems and methods for improving diagnostic tools for end users such as cardiologists and imaging specialists using machine learning techniques applied to specific problems associated with intravascular images that have polar representations. Further, given the use of rotating probes to obtain image data for OCT, IVUS, and other imaging data, dealing with the two coordinate systems associated therewith creates challenges. The present disclosure addresses these and numerous other challenges relating to solving the problem of quickly imaging and diagnosis a patient such that stenting and other procedures may be applied during a single session in the cath lab.
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:
In part, the disclosure relates to method for identifying regions of interest in a blood vessel. The method includes the steps of: providing OCT image data of the blood vessel; applying a plurality of different edge detection filters to the OCT image data to generate a filter response for each edge detection filter; identifying in each edge detection filter response any response maxima; combining the response maxima for each edge detection filter response while maintaining the spatial relationship of the response maxima, to thereby create edge filtered OCT data; and analyzing the edge filtered OCT data to identify a region of interest, the region of interest defined as a local cluster of response maxima. In one embodiment, one or more indicia are positioned in one or more panels to emphasize a reference vessel profile as part of a user interface.
Abstract:
In part, the disclosure relates to methods, and systems suitable for evaluating image data from a patient on a real time or substantially real time basis using machine learning (ML) methods and systems. Systems and methods for improving diagnostic tools for end users such as cardiologists and imaging specialists using machine learning techniques applied to specific problems associated with intravascular images that have polar representations. Further, given the use of rotating probes to obtain image data for OCT, IVUS, and other imaging data, dealing with the two coordinate systems associated therewith creates challenges. The present disclosure addresses these and numerous other challenges relating to solving the problem of quickly imaging and diagnosis a patient such that stenting and other procedures may be applied during a single session in the cath lab.
Abstract:
In part, the disclosure relates to an automated method of branch detection with regard to a blood vessel imaged using an intravascular modality such as OCT, IVUS, or other imaging modalities. In one embodiment, a representation of A-lines and frames generated using an intravascular imaging system is used to identify candidate branches of a blood vessel. One or more operators such as filters can be applied to remove false positives associated with other detections.
Abstract:
In part, the disclosure relates to method for identifying regions of interest in a blood vessel. The method includes the steps of: providing OCT image data of the blood vessel; applying a plurality of different edge detection filters to the OCT image data to generate a filter response for each edge detection filter; identifying in each edge detection filter response any response maxima; combining the response maxima for each edge detection filter response while maintaining the spatial relationship of the response maxima, to thereby create edge filtered OCT data; and analyzing the edge filtered OCT data to identify a region of interest, the region of interest defined as a local cluster of response maxima. In one embodiment, one or more indicia are positioned in one or more panels to emphasize a reference vessel profile as part of a user interface.