公开(公告)号：US20230309835A1

公开(公告)日：2023-10-05

申请号：US18128930

申请日：2023-03-30

Applicant: BOSTON SCIENTIFIC SCIMED, INC.

Inventor： Wenguang Li ,  Kevin Bloms ,  Zhichao Hong ,  Alan Torborg ,  Brandon Zingsheim

IPC: A61B5/00 ,  A61B5/02

CPC classification number: A61B5/0066 ,  A61B5/02007 ,  A61B8/0891

Abstract: This disclosure provides methods for vascular imaging co-registration and using the co-registered imaging data in guiding live fluoroscopy. Extravascular imaging data includes an extravascular contrast image showing the portion of the blood vessel with contrast showing a visualized anatomical landmark while intravascular imaging data is obtained during a translation procedure that includes one or more intravascular images showing a detected anatomical landmark. The starting location and the ending location of the imaging element on the extravascular imaging data is marked, and the predicted location of the detected anatomical landmark on the extravascular imaging data is marked. The predicted location of the detected anatomical landmark is then aligned with the visualized anatomical landmark.

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.