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公开(公告)号：US12141900B2
公开(公告)日：2024-11-12
申请号：US17543234
申请日：2021-12-06
Applicant: GE Precision Healthcare LLC
Inventor： Rajesh Veera Venkata Lakshmi Langoju , Utkarsh Agrawal , Bipul Das , Risa Shigemasa , Yasuhiro Imai , Jiang Hsieh
Abstract: Systems/techniques that facilitate machine learning generation of low-noise and high structural conspicuity images are provided. In various embodiments, a system can access an image and can apply at least one of image denoising or image resolution enhancement to the image, thereby yielding a first intermediary image. In various instances, the system can generate, via execution of a plurality of machine learning models, a plurality of second intermediary images based on the first intermediary image, wherein a given machine learning model in the plurality of machine learning models receives as input the first intermediary image, wherein the given machine learning model produces as output a given second intermediary image in the plurality of second intermediary images, and wherein the given second intermediary image represents a kernel-transformed version of the first intermediary image. In various cases, the system can generate a blended image based on the plurality of second intermediary images.
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2.发明公开公开(公告)号：US20240070861A1
公开(公告)日：2024-02-29
申请号：US18458030
申请日：2023-08-29
Applicant: GE Precision Healthcare LLC
Inventor： Yasuhiro Imai , Yuri Teraoka , Ayako Matsumi
IPC: G06T7/00
CPC classification number: G06T7/0012 , G06T2207/10072 , G06T2207/10116 , G06T2207/20081 , G06T2207/20084
Abstract: A device including one or more processor for performing an operation, the operation including inputting a CT image into a first trained neural network, causing the first trained neural network to infer a virtual monochromatic X-ray image based on the CT image, generating a water density image and iodine density image based on the CT image and the virtual monochromatic X-ray image inferred by the first trained neural network, inputting the water density image and iodine density image into a second trained neural network, and causing the second trained neural network to infer a water density image and iodine density image based on the water density image and iodine density image.
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公开(公告)号：US20230052595A1
公开(公告)日：2023-02-16
申请号：US17403017
申请日：2021-08-16
Applicant: GE Precision Healthcare LLC
Inventor： Rajesh Veera Venkata Lakshmi Langoju , Utkarsh Agrawal , Bipul Das , Risa Shigemasa , Yasuhiro Imai , Jiang Hsieh
Abstract: Techniques are described for enhancing the quality of three-dimensional (3D) anatomy scan images using deep learning. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a reception component that receives a scan image generated from 3D scan data relative to a first axis of a 3D volume, and an enhancement component that applies an enhancement model to the scan image to generate an enhanced scan image having a higher resolution relative to the scan image. The enhancement model comprises a deep learning neural network model trained on training image pairs respectively comprising a low-resolution scan image and a corresponding high-resolution scan image respectively generated relative to a second axis of the 3D volume.
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公开(公告)号：US20250049400A1
公开(公告)日：2025-02-13
申请号：US18929269
申请日：2024-10-28
Applicant: GE Precision Healthcare LLC
Inventor： Rajesh Langoju , Utkarsh Agrawal , Risa Shigemasa , Bipul Das , Yasuhiro Imai , Jiang Hsieh
Abstract: Various methods and systems are provided for computed tomography imaging. In one embodiment, a method includes acquiring, with an x-ray detector and an x-ray source coupled to a gantry, a three-dimensional image volume of a subject while the subject moves through a bore of the gantry and the gantry rotates the x-ray detector and the x-ray source around the subject, inputting the three-dimensional image volume to a trained deep neural network to generate a corrected three-dimensional image volume with a reduction in aliasing artifacts present in the three-dimensional image volume, and outputting the corrected three-dimensional image volume. In this way, aliasing artifacts caused by sub-sampling may be removed from computed tomography images while preserving details, texture, and sharpness in the computed tomography images.
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公开(公告)号：US20240265591A1
公开(公告)日：2024-08-08
申请号：US18164372
申请日：2023-02-03
Applicant: GE Precision Healthcare LLC
Inventor： Bipul Das , Utkarsh Agrawal , Prasad Sudhakara Murthy , Risa Shigemasa , Kentaro Ogata , Yasuhiro Imai
CPC classification number: G06T11/005 , G06T3/4053 , G06V10/44 , G06V10/806 , G06T2207/10081 , G06T2207/10116 , G06T2211/408
Abstract: Methods and systems are provided for interpolating missing views in dual-energy computed tomography data. In one example, a method includes obtaining a first sinogram missing a plurality of views and a second sinogram missing a different plurality of views, the first sinogram acquired with a first X-ray source energy during a scan and the second sinogram acquired with a second, different X-ray source energy during the scan; initializing each of the first sinogram and the second sinogram to form a first initialized sinogram and a second initialized sinogram; entering the first initialized sinogram and the second initialized sinogram into the same or different interpolation models trained to output a first filled sinogram based on the first initialized sinogram and output a second filled sinogram based on the second initialized sinogram; and reconstructing one or more images from the first filled sinogram and the second filled sinogram.
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Patent Agency Ranking
公开(公告)号：US12036054B2
公开(公告)日：2024-07-16
申请号：US18308369
申请日：2023-04-27
Applicant: GE Precision Healthcare LLC
Inventor： Yasuhiro Imai , Yoshihiro Oda , Ayako Matsumi
CPC classification number: A61B6/0492 , A61B6/032 , A61B6/0407
Abstract: The present disclosure relates to a system and method with which an imaged range in the past can be reproduced with high precision. In accordance with certain embodiments, an X-ray CT apparatus includes a camera-image producing section for producing a first camera image of a patient, a landmark fixing section for fixing a landmark with reference to a chest of the patient, and an imaged-range defining section for defining a first imaged range in a first scan based on landmark data representing the landmark. The camera-image producing section acquires a second camera image of the patient in a follow-up examination, the landmark fixing section fixes a second landmark with reference to the chest of the patient contained in the second image, and the imaged-range defining section defines second imaged range in a second scan based on landmark data representing the landmarks, and imaged-range data representing the first imaged range.
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公开(公告)号：US20240065645A1
公开(公告)日：2024-02-29
申请号：US18456884
申请日：2023-08-28
Applicant: GE Precision Healthcare LLC , Kansai Medical University
Inventor： Miyo Hattori , Yuri Teraoka , Ayako Matsumi , Yasuhiro Imai , Yuhei Koike
CPC classification number: A61B6/032 , A61B6/4208 , A61B6/461 , A61B6/482
Abstract: Systems and methods are described, which generate a plurality of virtual monochromatic X-ray images having different energy levels even with a CT system with single energy CT. An example CT system includes an X-ray tube in which a prescribed tube voltage (120 (kVp)) is applied and one or more processors. The one or more processors perform an operation including inputting a CT image generated based on the single energy CT data collected from a subject body to a trained neural network (94), and causing the trained neural network to infer 40 (keV), 50 (keV), 60 (keV), 80 (keV), 90 (keV), and 100 (keV) virtual monochromatic X-ray images based on the CT image.
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公开(公告)号：US20240062331A1
公开(公告)日：2024-02-22
申请号：US17821058
申请日：2022-08-19
Applicant: GE Precision Healthcare LLC
Inventor： Rajesh Langoju , Prasad Sudhakara Murthy , Utkarsh Agrawal , Risa Shigemasa , Bhushan Patil , Bipul Das , Yasuhiro Imai
CPC classification number: G06T3/4046 , G06T7/0012 , G06T5/002 , G06T7/11 , G06N3/08
Abstract: Systems/techniques that facilitate deep learning robustness against display field of view (DFOV) variations are provided. In various embodiments, a system can access a deep learning neural network and a medical image. In various aspects, a first DFOV, and thus a first spatial resolution, on which the deep learning neural network is trained can fail to match a second DFOV, and thus a second spatial resolution, exhibited by the medical image. In various instances, the system can execute the deep learning neural network on a resampled version of the medical image, where the resampled version of the medical image can exhibit the first DFOV and thus the first spatial resolution. In various cases, the system can generate the resampled version of the medical image by up-sampling or down-sampling the medical image until it exhibits the first DFOV and thus the first spatial resolution.
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9.发明申请公开(公告)号：US20250095239A1
公开(公告)日：2025-03-20
申请号：US18471181
申请日：2023-09-20
Applicant: GE Precision Healthcare LLC
Inventor： Rajesh Langoju , Utkarsh Agrawal , Bipul Das , Risa Shigemasa , Yasuhiro Imai , Kok Yen Tham , Yuri Teraoka
IPC: G06T11/00 , G06V10/764 , G06V10/774 , G06V20/50 , G16H30/40
Abstract: Various methods and systems are provided for transforming images from one energy level to another. In an example, a method includes obtaining an image at a first energy level acquired with a single-energy computed tomography (CT) imaging system, identifying a contrast phase of the image, entering the image as input into an energy transformation model trained to output a transformed image at a second energy level, different than the first energy level, the energy transformation model selected from among a plurality of energy transformation models based on the contrast phase, and displaying a final transformed image and/or saving the final transformed image in memory, wherein the final transformed image is the transformed image or is generated based on the transformed image.
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公开(公告)号：US20250095143A1
公开(公告)日：2025-03-20
申请号：US18471188
申请日：2023-09-20
Applicant: GE Precision Healthcare LLC
Inventor： Rajesh Langoju , Utkarsh Agrawal , Bipul Das , Risa Shigemasa , Yasuhiro Imai , Kok Yen Tham , Yuri Teraoka
Abstract: Methods and systems are provided for transforming images from one energy level to another. In an example, a method includes obtaining an image at a first energy level, identifying a contrast phase of the image, entering the image as input to a segmentation model trained to output an anatomy mask that identifies each tissue type in the image, generating a guide image from the image and the anatomy mask using a regression model, entering the image and the guide image as input into an energy transformation model trained to output a transformed image at a different, second energy level, the energy transformation model selected from among a plurality of energy transformation models based on the contrast phase, and displaying a final transformed image and/or saving the final transformed image in memory, wherein the final transformed image is the transformed image or is generated based on the transformed image.
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