公开(公告)号：US20250117931A1

公开(公告)日：2025-04-10

申请号：US18904145

申请日：2024-10-02

Applicant: Boston Scientific Scimed, Inc.

Inventor： Yan Li ,  Hatice Cinar Akakin ,  Wenguang Li ,  Kevin Bloms

IPC: G06T7/00 ,  G06T7/30 ,  G06T7/62 ,  G06V10/74

Abstract: The present disclosure provides to match side branches from different imaging modalities. Side branches detected from a series of intravascular images can be matched with side branches detected from an extravascular image. The pairs of matches can be determined based on an initially identified reference pair and then a technique that accounts for the characteristics of the side branch, such as, the diameter, the orientation, the relative size, and/or the relative order.

公开(公告)号：US20250117953A1

公开(公告)日：2025-04-10

申请号：US18904130

申请日：2024-10-02

Applicant: Boston Scientific Scimed, Inc.

Inventor： Yan Li ,  Hatice Cinar Akakin ,  Erik Stephen Freed ,  Kevin Bloms ,  Wenguang Li

IPC: G06T7/33

Abstract: The present disclosure provides to co-register intravascular images of a vessel with one or more extravascular images of the vessel in real-time, such as, during acquisition of the extravascular images. Key points in a first frame of the extravascular images can be identified and then tracked across other frames of the extravascular images. The intravascular images can be co-registered to a frame (or frames) of the extravascular images based on the locations of the key points in the frame.

公开(公告)号：US20250117952A1

公开(公告)日：2025-04-10

申请号：US18903068

申请日：2024-10-01

Applicant: Boston Scientific Scimed, Inc.

Inventor： Hatice Cinar Akakin ,  Kevin Bloms ,  Wenguang Li

IPC: G06T7/30 ,  G06T7/00 ,  G06T7/10

Abstract: The present disclosure provides to generate a side branch mask from an extravascular image of a vessel using an ensemble of machine learning (ML) models. The side branch mask can be generated by inferring, using several initial ML models, indications of side branches from an image frame and inferring, using a post-processing ML model, the side branch mask from the indication of side branches.

公开(公告)号：US20240428429A1

公开(公告)日：2024-12-26

申请号：US18667561

申请日：2024-05-17

Applicant: Boston Scientific Scimed Inc.

Inventor： Qian Li ,  Hatice Cinar Akakin ,  Alexander Shang ,  Wenguang Li

IPC: G06T7/33 ,  G06T7/73 ,  G06V10/82

Abstract: The present disclosure provides devices and methods to identify locations of side branches in a series of intravascular images (e.g., a pre-treatment IVUS pullback, a post-treatment IVUS pullback, or the like) to assist with co-registering the IVUS images with an extravascular image (e.g., angiogram, or the like) or with another set of IVUS images. The present disclosure further provides devices and methods for training a machine learning (ML) model to infer side branch locations from IVUS images and an analytic algorithm for extracting frames from the IVUS images representing side branches.

公开(公告)号：US20240386553A1

公开(公告)日：2024-11-21

申请号：US18666470

申请日：2024-05-16

Applicant: Boston Scientific Scimed Inc.

Inventor： Hatice Cinar Akakin ,  Kevin Bloms ,  Wenguang Li

IPC: G06T7/00 ,  G06V10/44 ,  G16H30/40

Abstract: The present disclosure provides devices and methods to process intravascular images of a vessel of one imaging modalities and to generate, extract and adapt features from another imaging modality to generate a hybrid image comprising features from both modalities. The disclosure provides devices and methods to train deep generative models to adapt domain specific features from one intravascular imaging modality (e.g., OCT, or the like) to another intravascular imaging modality (e.g., IVUS) and integrate the adapted features into the images from the other intravascular imaging modality.

公开(公告)号：US20230210381A1

公开(公告)日：2023-07-06

申请号：US18091772

申请日：2022-12-30

Applicant: Boston Scientific Scimed, Inc.

Inventor： Kevin Bloms ,  Wenguang Li ,  Hatice Cinar Akakin ,  Alexander Shang

IPC: A61B5/02 ,  A61B8/08

CPC classification number: A61B5/02028 ,  A61B8/0891 ,  G06T2207/30101 ,  G06N3/08

Abstract: A neural network is trained for estimating patient hemodynamic data using a plurality of extravascular imaging data sets and a plurality of intravascular imaging data sets that are each co-registered to a corresponding extravascular imaging data set. A plurality of hemodynamic data sets are provided, each hemodynamic data set co-registered with the corresponding extravascular imaging data set. The neural network learns what hemodynamic data to expect for a given intravascular imaging data set. An intravascular imaging event is subsequently performed in which an intravascular imaging element is translated within a blood vessel of the patient to produce one or more intravascular images. The neural network uses its training to predict hemodynamic values corresponding to the one or more intravascular images from the intravascular imaging event, and the one or more intravascular images are outputted in combination with the predicted hemodynamic values.