REAL-TIME LUMEN DISTANCE CALCULATION BASED ON THREE-DIMENSIONAL (3D) A-LINE SIGNAL DATA

    公开(公告)号:US20210407098A1

    公开(公告)日:2021-12-30

    申请号:US17354877

    申请日:2021-06-22

    Abstract: One or more devices, systems, methods, and storage mediums for optical imaging medical devices, such as, but not limited to, Optical Coherence Tomography (OCT), single mode OCT, and/or multi-modal OCT apparatuses and systems, and methods and storage mediums for use with same, for calculating lumen distance(s), including based on real-time A-line signal(s), are provided herein. Examples of applications include imaging, evaluating and diagnosing biological objects, such as, but not limited to, for Gastro-intestinal, cardio and/or ophthalmic applications, and being obtained via one or more optical instruments, such as, but not limited to, optical probes, catheters, capsules and needles (e.g., a biopsy needle). Fast A-line lumen segmentation methods, which can be applied real-time to a whole arterial pullback, and devices, systems, and storage mediums for use with same, are provided herein. Techniques provided herein also improve processing efficiency and decrease calculations while achieving measurements that are more precise.

    ARTIFACT REMOVAL FROM MULTIMODALITY OCT IMAGES

    公开(公告)号:US20230115191A1

    公开(公告)日:2023-04-13

    申请号:US17500583

    申请日:2021-10-13

    Abstract: Embodiments disclosed herein provide systems, methods and/or computer-readable media for automatically detecting and removing fluorescence artifacts from catheter-based multimodality OCT-NIRAF images. In one embodiment, a process of determining an automatic threshold value (automatic thresholding) is implemented by sorting characteristic parameter values of the NIRAF signal and finding a maximum perpendicular distance between a curve of the sorted values and a straight line from the highest to the lowest sorted value, combined with the use of unsupervised machine learning classification techniques to detect the frame's NIRAF values that correspond to signal artifacts. Once the signal artifacts are detected, the system can filter out the signal artifacts, correct the frames that had artifacts, and produce a more accurate multimodality image.

    Devices, systems, and methods for detecting external elastic lamina (EEL) from intravascular OCT images

    公开(公告)号:US12076118B2

    公开(公告)日:2024-09-03

    申请号:US17492376

    申请日:2021-10-01

    CPC classification number: A61B5/02007 A61B5/6852 A61B5/742 A61B2560/0223

    Abstract: One or more devices, systems, methods and storage mediums for optical imaging medical devices, such as, but not limited to, Optical Coherence Tomography (OCT), single mode OCT, and/or multi-modal OCT apparatuses and systems, and methods and storage mediums for use with same, for detecting external elastic lamina (EEL) and/or lumen edge(s) are provided herein. One or more embodiments provide at least one method, device, apparatus, system, or storage medium to comprehend information, including, but not limited to, molecular structure of a vessel, and to provide an ability to manipulate the vessel information. In addition to controlling one or more imaging modalities, the EEL detection techniques may operate for one or more applications, including, but not limited to, manual or automatic EEL detection, stent positioning, stent selection, co-registration, and imaging.

    SYSTEMS AND METHODS FOR ENDOVASCULAR DEVICE DETECTION AND APPOSITION MEASUREMENT

    公开(公告)号:US20220395230A1

    公开(公告)日:2022-12-15

    申请号:US17345797

    申请日:2021-06-11

    Abstract: Devices, systems, and methods for stent detection and apposition are disclosed. Embodiments obtain a plurality of images of intravascular image data of a vessel wall and an endovascular device, generate a signal that represents the plurality of images, identify one or more images that correspond to the endovascular device based on the signal that represents the plurality of images, generate a representation of a three-dimensional (3D) shape of the endovascular device based on the one or more images, determine an apposition value of the endovascular device relative to the vessel wall using a representation of a 3D shape of a lumen segment that corresponds to the endovascular device, the apposition value based on a volume difference between the 3D shape of the lumen segment and the 3D shape of the endovascular device, and present information indicating the apposition value.

    TISSUE CHARACTERIZATION IN ONE OR MORE IMAGES, SUCH AS IN INTRAVASCULAR IMAGES, USING ARTIFICIAL INTELLIGENCE

    公开(公告)号:US20250134387A1

    公开(公告)日:2025-05-01

    申请号:US18928722

    申请日:2024-10-28

    Abstract: One or more devices, systems, methods and storage mediums for performing intravascular imaging and/or optical coherence tomography (OCT) while detecting and/or characterizing one or more tissues are provided. Examples of applications include imaging, evaluating and diagnosing biological objects, such as, but not limited to, for Gastro-intestinal, cardio and/or ophthalmic applications, and being obtained via one or more optical instruments, such as, but not limited to, optical probes, catheters, capsules and needles (e.g., a biopsy needle). Preferably, the intravascular imaging devices, systems methods and storage mediums include or involve a method, such as, but not limited to, using one image, such as a carpet view, to detect and/or characterize the one or more tissues and/or to perform coregistration. Examples of identified or detected tissues include calcium, lipids, and other types of tissue.

    Artifact removal from multimodality OCT images

    公开(公告)号:US12112472B2

    公开(公告)日:2024-10-08

    申请号:US17500583

    申请日:2021-10-13

    Abstract: Embodiments disclosed herein provide systems, methods and/or computer-readable media for automatically detecting and removing fluorescence artifacts from catheter-based multimodality OCT-NIRAF images. In one embodiment, a process of determining an automatic threshold value (automatic thresholding) is implemented by sorting characteristic parameter values of the NIRAF signal and finding a maximum perpendicular distance between a curve of the sorted values and a straight line from the highest to the lowest sorted value, combined with the use of unsupervised machine learning classification techniques to detect the frame's NIRAF values that correspond to signal artifacts. Once the signal artifacts are detected, the system can filter out the signal artifacts, correct the frames that had artifacts, and produce a more accurate multimodality image.

    Real-time lumen distance calculation based on three-dimensional (3D) A-line signal data

    公开(公告)号:US11922633B2

    公开(公告)日:2024-03-05

    申请号:US17354877

    申请日:2021-06-22

    Abstract: One or more devices, systems, methods, and storage mediums for optical imaging medical devices, such as, but not limited to, Optical Coherence Tomography (OCT), single mode OCT, and/or multi-modal OCT apparatuses and systems, and methods and storage mediums for use with same, for calculating lumen distance(s), including based on real-time A-line signal(s), are provided herein. Examples of applications include imaging, evaluating and diagnosing biological objects, such as, but not limited to, for Gastro-intestinal, cardio and/or ophthalmic applications, and being obtained via one or more optical instruments, such as, but not limited to, optical probes, catheters, capsules and needles (e.g., a biopsy needle). Fast A-line lumen segmentation methods, which can be applied real-time to a whole arterial pullback, and devices, systems, and storage mediums for use with same, are provided herein. Techniques provided herein also improve processing efficiency and decrease calculations while achieving measurements that are more precise.

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