METHOD FOR EVALUATING CARDIAC MOTION USING AN ANGIOGRAPHY IMAGE

    公开(公告)号:US20190029623A1

    公开(公告)日:2019-01-31

    申请号:US16044931

    申请日:2018-07-25

    Inventor: Mie Kunio

    Abstract: Detecting a vessel region in multiple angiographic image frames and defining a direction that perpendicularly intersects a longitudinal direction of the vessel region to improve co-registration between two imaging modalities. Motion of the vessel region is then detected based on the direction that intersects the longitudinal direction of the vessel region by evaluating positions of the vessel region in the multiple angiographic image frames. The method includes defining an area based on the detected motion and the detected vessel region, detecting a marker of an imaging catheter disposed in the vessel region within the area and performing co-registration based on the detected marker.

    METHOD FOR DISPLAYING AN ANATOMICAL IMAGE OF A CORONARY ARTERY ON A GRAPHICAL USER INTERFACE

    公开(公告)号:US20180271614A1

    公开(公告)日:2018-09-27

    申请号:US15923956

    申请日:2018-03-16

    Inventor: Mie Kunio

    Abstract: A method for displaying an anatomical image of a coronary artery on a graphical user interface with information acquired from a plurality of intravascular image frames. The method may include detecting qualitative information from the plurality of intravascular image frames, creating one or more indicator(s) from the qualitative information detected, and determining a spatial relationship between the anatomical image and a plurality of acquisition locations of the plurality of intravascular image frames and generating its linear representation. The method also includes displaying the anatomical image of the coronary artery with the linear representation overlaid thereon on a display device and overlaying the one or more indicator(s) representing at least one type of qualitative information on the anatomical image along the linear representation.

    Artificial intelligence coregistration and marker detection, including machine learning and using results thereof

    公开(公告)号:US12161426B2

    公开(公告)日:2024-12-10

    申请号:US17761561

    申请日:2020-09-18

    Abstract: One or more devices, systems, methods, and storage mediums using artificial intelligence application(s) using an apparatus or system that uses and/or controls one or more imaging modalities, such as, but not limited to, angiography, Optical Coherence Tomography (OCT), Multi-modality OCT, near-infrared fluorescence (NIRAF), OCT-NIRAF, etc. are provided herein. Examples of AI applications discussed herein, include, but are not limited to, using one or more of: AI coregistration, AI marker detection, deep or machine learning, computer vision or image recognition task(s), keypoint detection, feature extraction, model training, input data preparation techniques, input mapping to the model, post-processing, and/or interpretation of output data, one or more types of machine learning models (including, but not limited to, segmentation, regression, combining or repeating regression and/or segmentation), marker detection success rates, and/or co-registration success rates to improve or optimize marker detection and/or co-registration.

    DRUG-COATED BALLOON DEVICE, SYSTEM, AND PROCEDURE

    公开(公告)号:US20240108864A1

    公开(公告)日:2024-04-04

    申请号:US17937157

    申请日:2022-09-30

    Abstract: There is provided medical devices and methods of use. The medical device comprising: an elongated tube; a balloon disposed over the elongate tube having at least one drug and at least one fluorescent agent on an outer surface of the balloon; an optical probe at the distal end of the elongate tube comprising an optical fiber configured to guide illumination light coming from a light source and an optical member configured for fluorescence imaging; and one or more detectors configured for fluorescence detection. The probe may comprise an optical probe for fluorescence imaging, and optionally an additional probe component for structural imaging or physiological sensing. The method can be particularly useful for determining whether sufficient dose of a drug has been transferred from a balloon to the lumen.

    Detecting and displaying stent expansion

    公开(公告)号:US11571129B2

    公开(公告)日:2023-02-07

    申请号:US16148421

    申请日:2018-10-01

    Abstract: A method for processing an intravascular image including a plurality of image frames acquired during a pullback of an imaging catheter inserted into a vessel, the method including displaying on a graphical user interface (GUI) an image including detected results of lumen borders and at least one stent, the image including an evaluated stent expansion and an evaluated stent apposition determined from the intravascular image. The method also includes determining whether a modification to the detected results of the stent has been received by the GUI. Then, re-evaluating stent length, stent expansion and stent apposition when it is determined that the detected results of the stent has been modified via the GUI and displaying the re-evaluated stent expansion and the re-evaluated stent apposition on the GUI.

    ARTIFICIAL INTELLIGENCE COREGISTRATION AND MARKER DETECTION, INCLUDING MACHINE LEARNING AND USING RESULTS THEREOF

    公开(公告)号:US20220346885A1

    公开(公告)日:2022-11-03

    申请号:US17761561

    申请日:2020-09-18

    Abstract: One or more devices, systems, methods, and storage mediums using artificial intelligence application(s) using an apparatus or system that uses and/or controls one or more imaging modalities, such as, but not limited to, angiography, Optical Coherence Tomography (OCT), Multi-modality OCT, near-infrared fluorescence (NIRAF), OCT-NIRAF, etc. are provided herein. Examples of AI applications discussed herein, include, but are not limited to, using one or more of: AI coregistration, AI marker detection, deep or machine learning, computer vision or image recognition task(s), keypoint detection, feature extraction, model training, input data preparation techniques, input mapping to the model, post-processing, and/or interpretation of output data, one or more types of machine learning models (including, but not limited to, segmentation, regression, combining or repeating regression and/or segmentation), marker detection success rates, and/or coregistration success rates to improve or optimize marker detection and/or coregistration.

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