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    NETWORK ARCHITECTURE FOR 3D IMAGE PROCESSING
    1.
    发明申请
    NETWORK ARCHITECTURE FOR 3D IMAGE PROCESSING 有权

    公开(公告)号：US20240420325A1

    公开(公告)日：2024-12-19

    申请号：US18721224

    申请日：2022-12-19

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： ASHER GRINGAUZ ,  LIRAN GOSHEN

    IPC: G06T7/00

    Abstract: A mechanism for processing input 3D image data. In a first phase, the input 3D image data is separately processed using one or more neural networks to produce one or more modified 3D image data. In a second phase, the input 3D image data and the modified 3D image data are processed using neural networks to produce an output. The neural networks that produce the modified 3D image data are configured to process slices or sub-volumes of the input 3D image data to produce modified 3D image data.

    SPECTRAL INFLAMMATION MAP FROM SPECTRAL IMAGING DATA
    2.
    发明申请
    SPECTRAL INFLAMMATION MAP FROM SPECTRAL IMAGING DATA 有权

    公开(公告)号：US20210007698A1

    公开(公告)日：2021-01-14

    申请号：US17041172

    申请日：2019-03-22

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： MORDECHAY PINCHAS FREIMAN ,  LIRAN GOSHEN

    IPC: A61B6/00 ,  G06T7/00 ,  G06T15/08

    Abstract: A system (300) includes a memory (324) configured to store an inflammation map generator module (328). The system further includes a processor (322) configured to: receive at least one of spectral projection data or spectral volumetric image data, decompose the at least one of spectral projection data or spectral volumetric image data using a two-basis decomposition to generate a set of vectors for each basis represented in the at least one of spectral projection data or spectral volumetric image data, compute a concentration of each basis within a voxel from the set of vectors for each basis, and determine a concentration of at least one of fat or inflammation within the voxel from the concentration of each basis. The system further includes a display configured to display the determined concentration of the at least one of fat or inflammation.

    IMAGE RENDERING METHOD FOR TOMOGRAPHIC IMAGE DATA
    5.
    发明公开
    IMAGE RENDERING METHOD FOR TOMOGRAPHIC IMAGE DATA 审中-公开

    公开(公告)号：US20230334732A1

    公开(公告)日：2023-10-19

    申请号：US18035121

    申请日：2021-10-28

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： RAFAEL WIEMKER ,  DANIEL BYSTROV ,  LIRAN GOSHEN

    IPC: G06T11/00 ,  G06T15/08 ,  G06T15/06

    CPC classification number: G06T11/008 ,  G06T15/08 ,  G06T15/06 ,  G06T2211/40 ,  G06T2210/41

    Abstract: A method for generating an image representation of slices through a body based on tomographic imaging data for the body. The method comprises processing reconstructed tomographic image slices to selectively embed in each slice image information from at least one 3D volume rendering of the slice plane within the 3D tomographic image dataset. This is done through a selection process wherein, based on a set of pre-defined criteria, a decision is made for each pixel in each reconstructed tomographic slice as to whether the pixel value should be replaced with a new, modified pixel value determined based on the at least one volume rendering. This may comprise simply swapping the pixel value for the value of the corresponding pixel value in the volume rendering, or it may comprise a more complex process, for instance blending the two values, or adjusting a transparency of the pixel value based on the at least one volume rendering.

    TRAINING DATA SYNTHESIZER FOR CONTRAST ENHANCING MACHINE LEARNING SYSTEMS
    6.
    发明公开
    TRAINING DATA SYNTHESIZER FOR CONTRAST ENHANCING MACHINE LEARNING SYSTEMS 审中-公开

    公开(公告)号：US20240312086A1

    公开(公告)日：2024-09-19

    申请号：US18276665

    申请日：2022-02-08

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： LIRAN GOSHEN

    IPC: G06T11/60 ,  G06T5/70

    CPC classification number: G06T11/60 ,  G06T5/70 ,  G06T2207/30168

    Abstract: A system (DSS) and related method for synthesizing training data or machine learning, based on a set (TD) including two types of training imagery, high image quality, IQ, imagery and low IQ imagery. The system comprises a data synthesizer (DSY), configured to register the at least two types of imagery and to transfer i) image information from high IQ imagery to the registered low IQ imagery to obtain synthesized high IQ imagery, or ii) image information from low IQ imagery to the registered high IQ imagery to obtain synthesized low IQ imagery. The synthesized data may be used for improved training of machine learning models for IQ enhancement.

    CONTRAST BOOST BY MACHINE LEARNING
    7.
    发明公开
    CONTRAST BOOST BY MACHINE LEARNING 审中-公开

    公开(公告)号：US20240311974A1

    公开(公告)日：2024-09-19

    申请号：US18276677

    申请日：2022-02-14

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： LIRAN GOSHEN

    IPC: G06T5/60 ,  G06T5/70 ,  G06T5/94

    CPC classification number: G06T5/60 ,  G06T5/70 ,  G06T5/94 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10116 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004

    Abstract: A training system for a target machine learning model for image enhancement, and related methods. The system comprises a framework of two machine learning models (G1, G2) of the generative type, one such model (G1) being part of the target machine learning model. The training is based on a training data set including at least two types of training imagery, high image quality, IQ, imagery and low IQ imagery, the training input image (I) being one of the high IQ type. The generative network (G1) processes the training input image (I) of the high IQ type to produce a training output image (I) having reduced IQ. The target machine learning model (TM) further produces, based on the training output image (I) and the training input image (I) of the high IQ type, a second training output image (I). The second generator network (G2) estimates, from the second training output image (I), an estimate of the training input image of the high IQ type. A training controller (TC) adjusts parameters of the machine learning model framework, based a deviation between the estimate of the training input image of the high IQ type, and the said training input image (I) of the high IQ type.

    IMAGE FEATURE ANNOTATION IN DIAGNOSTIC IMAGING
    9.
    发明申请
    IMAGE FEATURE ANNOTATION IN DIAGNOSTIC IMAGING 审中-公开

    公开(公告)号：US20200258615A1

    公开(公告)日：2020-08-13

    申请号：US16753792

    申请日：2018-10-02

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： LIRAN GOSHEN

    IPC: G16H30/40 ,  G06T7/00 ,  G06T11/00 ,  G06T7/11 ,  G06T15/08

    Abstract: The present invention relates to an image processing device (10) comprising a data input (11) for receiving volumetric image data comprising a plurality of registered volumetric images of an imaged object, a noise modeler (12) for generating a noise model indicative of a spatial distribution of noise in each of the plurality of registered volumetric images, a feature detector (13) for detecting a plurality of image features taking the volumetric image data into account, and a marker generator (14) for generating a plurality of references indicating feature positions of a subset of the plurality of detected image features, in which said subset corresponds to the detected image features that are classified as difficult to discern on a reference volumetric image in the plurality of registered volumetric images based on a classification and/or a visibility criterium, wherein the classification and/or the visibility criterium takes the or each noise model into account.

    METHOD FOR IMAGE-PROCESSING OF CT IMAGES
    10.
    发明申请
    METHOD FOR IMAGE-PROCESSING OF CT IMAGES 有权

    公开(公告)号：US20250061549A1

    公开(公告)日：2025-02-20

    申请号：US18719922

    申请日：2022-12-16

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： ASHER GRINGAUZ ,  LIRAN GOSHEN

    IPC: G06T5/70 ,  G06T5/20

    Abstract: The invention provides a computer-implemented method for image-processing of CT images, the method comprising performing one or more pre-processing steps on a CT image so as to obtain a pre-processed CT image, wherein the one or more pre-processing steps comprise applying an edge-preserving denoising algorithm; and performing an adaptive spike suppression algorithm on the pre-processed CT image to obtain a processed CT image, the adaptive spike suppression algorithm being configured such that the processed CT image has a reduced number of spikes as compared to the pre-processed CT image.

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