公开(公告)号：US11763429B2

公开(公告)日：2023-09-19

申请号：US17325010

申请日：2021-05-19

Applicant: GE Precision Healthcare LLC

Inventor： Sudhanya Chatterjee ,  Dattesh Dayanand Shanbhag

IPC: G06K9/00 ,  G06T5/00 ,  G06T7/00 ,  G06N3/08

CPC classification number: G06T5/002 ,  G06N3/08 ,  G06T7/0002 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: A medical imaging system having at least one medical imaging device providing image data of a subject is provided. The medical imaging system further includes a processing system programmed to train a deep learning (DL) network using a plurality of training images to predict noise in input data. The plurality of training images includes a plurality of excitation (NEX) images acquired for each line of k-space training data. The processing system is further programmed to use the trained DL network to determine noise in the image data of the subject and to generate a denoised medical image of the subject having reduced noise based on the determined noise in the image data.

公开(公告)号：US20230290487A1

公开(公告)日：2023-09-14

申请号：US18319686

申请日：2023-05-18

Applicant: GE Precision Healthcare LLC

Inventor： Hariharan Ravishankar ,  Dattesh Dayanand Shanbhag

IPC: G16H30/40 ,  G16H30/20 ,  A61B5/055 ,  A61B6/03 ,  G06T7/00 ,  G06T11/00 ,  G06N3/045

CPC classification number: G16H30/40 ,  G16H30/20 ,  A61B5/055 ,  A61B6/032 ,  G06T7/0014 ,  G06T11/008 ,  G06N3/045 ,  G06T2207/10081 ,  G06T2207/10084 ,  G06T2207/10088

Abstract: Methods and systems are provided for reconstructing images from measurement data using one or more deep neural networks according to a decimation strategy. In one embodiment, a method for reconstructing an image using measurement data comprises, receiving measurement data acquired by an imaging device, selecting a decimation strategy, producing a reconstructed image from the measurement data using the decimation strategy and one or more deep neural networks, and displaying the reconstructed image via a display device. By decimating measurement data to form one or more decimated measurement data arrays, a computational complexity of mapping the measurement data to image data may be reduced from O(N4), where N is the size of the measurement data, to O(M4), where M is the size of an individual decimated measurement data array, wherein M

公开(公告)号：US20230162487A1

公开(公告)日：2023-05-25

申请号：US18099530

申请日：2023-01-20

Applicant: GE PRECISION HEALTHCARE LLC

Inventor： Soumya Ghose ,  Desmond Teck Beng Yeo ,  Thomas Kwok-Fah Foo ,  Chitresh Bhushan ,  Deepa Anand ,  Dattesh Dayanand Shanbhag ,  Radhika Madhavan

IPC: G06V10/774 ,  G06N3/098 ,  G06V10/762

CPC classification number: G06V10/774 ,  G06N3/098 ,  G06V10/763

Abstract: A computer implemented method is provided. The method includes establishing, via multiple processors, a continuous federated learning framework including a global model at a global site and respective local models derived from the global model at respective local sites. The method also includes retraining or retuning, via the multiple processors, the global model and the respective local models without sharing actual datasets between the global site and the respective local sites but instead sharing synthetic datasets generated from the actual datasets.

公开(公告)号：US20220358692A1

公开(公告)日：2022-11-10

申请号：US17307517

申请日：2021-05-04

Applicant: GE Precision Healthcare LLC

Inventor： Chitresh Bhushan ,  Dattesh Dayanand Shanbhag ,  Rakesh Mullick

IPC: G06T11/00 ,  G06T7/11 ,  G06K9/32 ,  G06T7/80 ,  G06T7/00 ,  A61B5/055 ,  A61B5/00

Abstract: Techniques are described for generating reformatted views of a three-dimensional (3D) anatomy scan using deep-learning estimated scan prescription masks. 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 mask generation component that employs a pre-trained neural network model to generate masks for different anatomical landmarks depicted in one or more calibration images captured of an anatomical region of a patient. The computer executable components further comprise a reformatting component that reformats 3D image data captured of the anatomical region of the patient using the masks to generate different representations of the 3D image data that correspond to the different anatomical landmarks.

公开(公告)号：US20210177295A1

公开(公告)日：2021-06-17

申请号：US16711120

申请日：2019-12-11

Applicant: GE Precision Healthcare LLC

Inventor： André de Almeida Maximo ,  Dattesh Dayanand Shanbhag ,  Chitresh Bhushan ,  Dawei Gui

IPC: A61B5/055 ,  G01R33/54 ,  G06T7/00

Abstract: Methods and systems are provided for determining diagnostic-scan parameters for a magnetic resonance (MR) diagnostic-scan, from MR calibration images, enabling acquisition of high-resolution diagnostic images of one or more anatomical regions of interest, while bypassing acquisition of localizer images, increasing a speed and efficiency of MR diagnostic-scanning. In one embodiment, a method for a magnetic resonance imaging (MRI) system comprises, acquiring a magnetic resonance (MR) calibration image of an imaging subject, mapping the MR calibration image to a landmark map using a trained deep neural network, determining one or more diagnostic-scan parameters based on the landmark map, acquiring an MR diagnostic image according to the diagnostic-scan parameters, and displaying the MR diagnostic image via a display device.

公开(公告)号：US20250104270A1

公开(公告)日：2025-03-27

申请号：US18475406

申请日：2023-09-27

Applicant: GE Precision Healthcare LLC

Inventor： Deepa Anand ,  Dattesh Dayanand Shanbhag ,  Chitresh Bhushan ,  Dawei Gui ,  Kavitha Manickam ,  Maggie MeiKei Fung ,  Gurunath Reddy Madhumani

IPC: G06T7/73 ,  G06T7/00 ,  G06V10/25 ,  G06V10/26 ,  G06V10/44 ,  G06V10/762 ,  G06V10/774 ,  G06V20/70

Abstract: A method for performing one-shot anatomy localization includes obtaining a medical image of a subject. The method includes receiving a selection of both a template image and a region of interest within the template image, wherein the template image includes one or more anatomical landmarks assigned a respective anatomical label. The method includes inputting both the medical image and the template image into a trained vision transformer model. The method includes outputting from the trained vision transformer model both patch level features and image level features for both the medical image and the template image. The method still further includes interpolating pixel level features from the patch level features for both the medical image and the template image. The method includes utilizing the pixel level features within the region of interest of the template image to locate and label corresponding pixel level features in the medical image.

公开(公告)号：US12087433B2

公开(公告)日：2024-09-10

申请号：US18319686

申请日：2023-05-18

Applicant: GE Precision Healthcare LLC

Inventor： Hariharan Ravishankar ,  Dattesh Dayanand Shanbhag

IPC: G16H30/40 ,  A61B5/055 ,  A61B6/03 ,  G06N3/045 ,  G06T7/00 ,  G06T11/00 ,  G16H30/20

CPC classification number: G16H30/40 ,  A61B5/055 ,  A61B6/032 ,  G06N3/045 ,  G06T7/0014 ,  G06T11/008 ,  G16H30/20 ,  G06T2207/10081 ,  G06T2207/10084 ,  G06T2207/10088

Abstract: Methods and systems are provided for reconstructing images from measurement data using one or more deep neural networks according to a decimation strategy. In one embodiment, a method for reconstructing an image using measurement data comprises, receiving measurement data acquired by an imaging device, selecting a decimation strategy, producing a reconstructed image from the measurement data using the decimation strategy and one or more deep neural networks, and displaying the reconstructed image via a display device. By decimating measurement data to form one or more decimated measurement data arrays, a computational complexity of mapping the measurement data to image data may be reduced from O(N4), where N is the size of the measurement data, to O(M4), where M is the size of an individual decimated measurement data array, wherein M

公开(公告)号：US12078697B1

公开(公告)日：2024-09-03

申请号：US18111147

申请日：2023-02-17

Applicant: GE Precision Healthcare LLC

Inventor： Kavitha Manickam ,  Dattesh Dayanand Shanbhag ,  Dawei Gui ,  Chitresh Bhushan

IPC: G01R33/28 ,  G01R33/54

CPC classification number: G01R33/288 ,  G01R33/546

Abstract: A computer-implemented method for performing a scan of a subject utilizing a magnetic resonance imaging (MRI) system includes initiating, via a processor, a prescan of the subject by an MRI scanner of the MRI system without a priori knowledge as to whether the subject has a metal implant. The computer-implemented method also includes executing, via the processor, a metal detection algorithm during a prescan entry point of the prescan to detect whether the metal implant is present in the subject. The computer-implemented method further includes determining, via the processor, to proceed with a calibration scan and the scan utilizing predetermined scan parameters when no metal implant is detected in the subject. The computer-implemented method even further includes switching, via the processor, into a metal implant scan mode when one or more metal implants are detected in the subject.

公开(公告)号：US20240029415A1

公开(公告)日：2024-01-25

申请号：US17814746

申请日：2022-07-25

Applicant: GE Precision Healthcare LLC

Inventor： Dattesh Dayanand Shanbhag ,  Chitresh Bhushan ,  Soumya Ghose ,  Deepa Anand

IPC: G06V10/774 ,  G06V10/77 ,  G06T19/20 ,  G06T15/08 ,  G06T7/00 ,  G16H30/40 ,  G16H50/50

CPC classification number: G06V10/7747 ,  G06V10/7715 ,  G06T19/20 ,  G06T15/08 ,  G06T7/0014 ,  G16H30/40 ,  G16H50/50 ,  G06T2207/20081 ,  G06T2207/30096 ,  G06T2207/30012 ,  G06V2201/033 ,  G06T2219/2021 ,  G06T2210/41

Abstract: Systems and methods are provided for an image processing system. In an example, a method includes acquiring a pathology dataset, acquiring a reference dataset, generating a deformation field by mapping points of a reference case of the reference dataset to points of a patient image of the pathology dataset, manipulating the deformation field, applying the deformation field to the reference case to generate a simulated pathology image including a simulated deformation pathology, and outputting the simulated pathology image.

公开(公告)号：US20230342913A1

公开(公告)日：2023-10-26

申请号：US17660717

申请日：2022-04-26

Applicant: GE Precision Healthcare LLC

Inventor： Mahendra Madhukar Patil ,  Rakesh Mullick ,  Sudhanya Chatterjee ,  Syed Asad Hashmi ,  Dattesh Dayanand Shanbhag ,  Deepa Anand ,  Suresh Emmanuel Devadoss Joel

IPC: G06T7/00 ,  G06N20/00 ,  G06V10/25

CPC classification number: G06T7/0012 ,  G06N20/00 ,  G06V10/25 ,  G06V2201/03

Abstract: Techniques are described for generating high quality training data collections for training artificial intelligence (AI) models in the medical imaging domain. A method embodiment comprises receiving, by a system comprising processor, input indicating a clinical context associated with usage of a medical image dataset, and selecting, by the system, one or more data scrutiny metrics for filtering the medical image dataset based on the clinical context. The method further comprises applying, by the system, one or more image processing functions to the medical image dataset to generate metric values of the one or more data scrutiny metrics for respective medical images included in the medical image dataset, filtering, by the system, the medical image dataset into one or more subsets based on one or more acceptability criteria for the metric values.