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    RADIOLOGY QUALITY DASHBOARD DATA ANALYSIS AND INSIGHT ENGINE
    1.
    发明申请
    RADIOLOGY QUALITY DASHBOARD DATA ANALYSIS AND INSIGHT ENGINE 有权

    公开(公告)号：US20230113060A1

    公开(公告)日：2023-04-13

    申请号：US17913252

    申请日：2021-03-16

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： PRESCOTT PETER KLASSEN ,  TIM PHILIPP HARDER ,  THOMAS BUELOW

    IPC: G16H40/20 ,  G16H30/20

    Abstract: A non-transitory computer readable medium (26) stores instructions readable and executable by at least one electronic processor (20) to provide statistical analysis on one or more radiology databases (30, 32) in conjunction with a workstation (18) having a display device (24) and at least one user input device (22). The instructions include: instructions readable and executable by the at least one electronic processor to provide a plurality of different analysis services (39) for selecting and processing exam data stored in the one or more radiology databases; and user guidance instructions readable and executable by the at least one electronic processor to guide a user in creating and executing a workflow (38) by providing a graphical user interface (GUI) (28) on the workstation having menus nd/or GUI dialogs (40) for user selection, configuration, and execution of an ordered sequence of analysis services and providing data persistence between the analysis services of the ordered sequence of analysis services.

    MEDICAL IMAGING AND ANALYSIS METHOD
    3.
    发明申请
    MEDICAL IMAGING AND ANALYSIS METHOD 有权

    公开(公告)号：US20240404055A1

    公开(公告)日：2024-12-05

    申请号：US18695880

    申请日：2022-09-29

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： HRISHIKESH NARAYANRAO DESHPANDE ,  THOMAS BUELOW ,  AXEL SAALBACH ,  TIM PHILIPP HARDER ,  SHLOMO GOTMAN ,  EDNA COETSER

    IPC: G06T7/00

    Abstract: A method is provided for analyzing survey imaging data. The method comprises acquiring first image data with a first imaging protocol covering a first FOV, processing the data with an anatomical analysis program or routine to detect at least a portion of a target anatomy, and performing a coverage check adapted to determine whether the target anatomy is fully covered within the first FOV. Second image data is subsequently acquired in accordance with a second imaging protocol defining a second FOV. The second imaging protocol may be: the same as the first, but wherein the results of the coverage check are stored and linked with the second image data for later use; different to the first and wherein the results of the coverage check are output to a user interface and a user input responsive thereto is used to determine the second scan protocol; or different to the first, but wherein an adjusted second scan protocol is automatically determined.

    DEEP LEARNING BASED PROCESSING OF MOTION ARTIFACTS IN MAGNETIC RESONANCE IMAGING DATA
    4.
    发明申请
    DEEP LEARNING BASED PROCESSING OF MOTION ARTIFACTS IN MAGNETIC RESONANCE IMAGING DATA 有权

    公开(公告)号：US20210181287A1

    公开(公告)日：2021-06-17

    申请号：US16759778

    申请日：2018-10-22

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： KARSTEN SOMMER ,  TOM BROSCH ,  TIM PHILIPP HARDER ,  JOCHEN KEUPP ,  INGMAR GRAESSLIN ,  RAFAEL WIEMKER ,  AXEL SAALBACH

    IPC: G01R33/565 ,  G01R33/56 ,  G06T5/00 ,  G06T7/00 ,  G06T5/50 ,  G06T5/20 ,  G06N3/08 ,  G06N3/04

    Abstract: The invention relates to a magnetic resonance imaging data processing system (126) for processing motion artifacts in magnetic resonance imaging data sets using a deep learning network (146, 502, 702) trained for the processing of motion artifacts in magnetic resonance imaging data sets. The magnetic resonance imaging data processing system (126) comprises a memory (134, 136) storing machine executable instructions (161, 164) and the trained deep learning network (146, 502, 702). Furthermore, the magnetic resonance imaging data processing system (126) comprises a processor (130) for controlling the magnetic resonance imaging data processing system. Execution of the machine executable instructions (161, 164) causes the processor (130) to control the magnetic resonance imaging data processing system (126) to: receive a magnetic resonance imaging data set (144, 500, 800), apply the received magnetic resonance imaging data set (144, 500, 800) as an input to the trained deep learning network (146, 502, 702), process one or more motion artifacts present in the received magnetic resonance imaging data set (144, 500, 800) using the trained deep learning network (146, 502, 702).

    METHOD FOR CONFIGURING A MEDICAL DEVICE
    7.
    发明申请
    METHOD FOR CONFIGURING A MEDICAL DEVICE 审中-公开

    公开(公告)号：US20200008704A1

    公开(公告)日：2020-01-09

    申请号：US16470274

    申请日：2017-12-18

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： EBERHARD SEBASTIAN HANSIS ,  MICHAEL GUNTER HELLE ,  TIM PHILIPP HARDER ,  THOMAS NETSCH

    IPC: A61B5/055 ,  A61B6/00 ,  G16H40/63

    Abstract: The present disclosure relates to a method for configuring a medical device. The method comprises: providing a set of one or more parameters for configuring the medical device. Each parameter of the set has predefined values. A set of values of the set of parameters may be selected from the predefined values. Using the selected values the set of parameters may be set, which results in an operational configuration of the medical device. The medical device may be controlled to operate in accordance with the operational configuration, thereby an operating status of the medical device may be determined. Based on at least the operating status the operational configuration may be maintained or the selecting, setting and controlling may be repeatedly performed until a desired operating status of the medical device can be determined based on the operating statuses resulting from the controlling.

    BASELINE IMAGE GENERATION FOR DIAGNOSTIC APPLICATIONS
    8.
    发明申请
    BASELINE IMAGE GENERATION FOR DIAGNOSTIC APPLICATIONS 有权

    公开(公告)号：US20250046428A1

    公开(公告)日：2025-02-06

    申请号：US18717494

    申请日：2022-12-06

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： AXEL SAALBACH ,  TIM PHILIPP HARDER ,  THOMAS BUELOW ,  ANDRE GOOSSEN ,  SVEN KROENKE-HILLE ,  JENS VON BERG ,  MICHAEL GRASS

    IPC: G16H30/40 ,  G06F3/04842 ,  G06T5/77 ,  G06V10/82 ,  G16H30/20 ,  G16H50/20 ,  G16H50/70

    Abstract: Technology provides baseline images for diagnostic applications, including receiving a diagnostic image relating to a condition of a patient, the diagnostic image reflecting one of a normal state or an abnormal state of the condition, and generating a baseline image via a neural network using the diagnostic image, where the neural network is trained to generate a prediction of the diagnostic image reflecting a normal state of the condition. The neural network can include a generative adversarial network (GAN) trained only on image data with a normal state of the condition, where generating the baseline image includes an optimization process to maximize a similarity between the diagnostic image and a response of the GAN. Generating the baseline image can include selecting a portion of the diagnostic image, and adjusting a relevance weighting to be applied to the selected portion of the diagnostic image in the optimization process.

    OBJECT VISUALISATION IN X-RAY IMAGING
    9.
    发明申请
    OBJECT VISUALISATION IN X-RAY IMAGING 有权

    公开(公告)号：US20240366162A1

    公开(公告)日：2024-11-07

    申请号：US18687635

    申请日：2022-08-25

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： STEWART MATTHEW YOUNG ,  DANIEL BYSTROV ,  SVEN KROENKE-HILLE ,  HEINER MATTHIAS BRUECK ,  JENS VON BERG ,  TIM PHILIPP HARDER ,  ANDRÉ GOOSSEN, Sr.

    IPC: A61B6/08 ,  A61B6/00 ,  A61B6/46

    Abstract: An X-ray imaging system (100) includes an X-ray source (110) and an X-ray detector (120) that are separated by an examination region (150) for performing an X-ray imaging operation on an object (160). A processor (140) is configured to identify (S120) one or more internal structures (180) within the object (160), based on a comparison of depth sensor data representing a three-dimensional surface (170) of the object (160), with an anatomical model comprising the one or more internal structures (180). The processor (140) is also configured to compute (S130), using the depth sensor data and the identified one or more internal structures (180), a surface projection (190) of the one or more internal structures, on the surface (170) of the object (160), from a perspective of the X-ray source (110); and to output (S140) an image representation of the surface projection (190) for displaying as an overlay on the surface (170) of the object (160).

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