公开(公告)号：US11808832B2

公开(公告)日：2023-11-07

申请号：US17344274

申请日：2021-06-10

Applicant: GE PRECISION HEALTHCARE LLC

Inventor： Sudhanya Chatterjee ,  Dattesh Dayanand Shanbhag

IPC: G06T7/00 ,  G01R33/565 ,  G06N3/084

CPC classification number: G01R33/56554 ,  G06N3/084 ,  G06T7/0012 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: A computer-implemented method for generating an artifact corrected reconstructed contrast image from magnetic resonance imaging (MRI) data is provided. The method includes inputting into a trained deep neural network both a synthesized contrast image derived from multi-delay multi-echo (MDME) scan data or the MDME scan data acquired during a first scan of an object of interest utilizing a MDME sequence and a composite image, wherein the composite image is derived from both the MDME scan data and contrast scan data acquired during a second scan of the object of interest utilizing a contrast MRI sequence. The method also includes utilizing the trained deep neural network to generate the artifact corrected reconstructed contrast image based on both the synthesized contrast image or the MDME scan data and the composite image. The method further includes outputting from the trained deep neural network the artifact corrected reconstructed contrast image.

公开(公告)号：US20230341914A1

公开(公告)日：2023-10-26

申请号：US18343258

申请日：2023-06-28

Applicant: GE Precision Healthcare LLC

Inventor： Chitresh Bhushan ,  Dattesh Dayanand Shanbhag ,  Rakesh Mullick

IPC: G06F1/26 ,  H03F3/45 ,  G05F1/46 ,  G06F13/16 ,  G06F13/42 ,  H03K5/1252

CPC classification number: G06F1/266 ,  H03F3/45475 ,  G05F1/46 ,  G06F13/1668 ,  G06F13/4282 ,  H03K5/1252 ,  G06F2213/0026

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：US11699515B2

公开(公告)日：2023-07-11

申请号：US17364544

申请日：2021-06-30

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 ,  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：US20230094940A1

公开(公告)日：2023-03-30

申请号：US17486796

申请日：2021-09-27

Applicant: GE Precision Healthcare LLC

Inventor： Radhika Madhavan ,  Soumya Ghose ,  Dattesh Dayanand Shanbhag ,  Andre De Almeida Maximo ,  Chitresh Bhushan ,  Desmond Teck Beng Yeo ,  Thomas Kwok-Fah Foo

IPC: G06N3/08 ,  G06K9/62

Abstract: A deep learning-based continuous federated learning network system is provided. The system includes a global site comprising a global model and a plurality of local sites having a respective local model derived from the global model. The plurality of model tuning modules having a processing system are provided at the plurality of local sites for tuning the respective local model. The processing system is programmed to receive incremental data and select one or more layers of the local model for tuning based on the incremental data. Finally, the selected layers are tuned to generate a retrained model.

公开(公告)号：US20220114389A1

公开(公告)日：2022-04-14

申请号：US17067179

申请日：2020-10-09

Applicant: GE Precision Healthcare LLC

Inventor： Soumya Ghose ,  Dattesh Dayanand Shanbhag ,  Chitresh Bhushan ,  Andre De Almeida Maximo ,  Radhika Madhavan ,  Desmond Teck Beng Yeo ,  Thomas Kwok-Fah Foo

IPC: G06K9/62 ,  G06N3/08 ,  G16H30/40

Abstract: A computer-implemented method of automatically labeling medical images is provided. The method includes clustering training images and training labels into clusters, each cluster including a representative template having a representative image and a representative label. The method also includes training a neural network model with a training dataset that includes the training images and the training labels, and target outputs of the neural network model are labels of the medical images. The method further includes generating a suboptimal label corresponding to an unlabeled test image using the trained neural network model, and generating an optimal label corresponding to the unlabeled test image using the suboptimal label and representative templates. In addition, the method includes updating the training dataset using the test image and the optimal label, retraining the neural network model, generating a label of an unlabeled image using the retrained neural network model, and outputting the generated label.

公开(公告)号：US20210080531A1

公开(公告)日：2021-03-18

申请号：US16573955

申请日：2019-09-17

Applicant: GE Precision Healthcare LLC

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

IPC: G01R33/58 ,  G01R33/54 ,  A61B5/055 ,  A61B5/00 ,  G06N3/08 ,  G06N20/00 ,  G06F9/54

Abstract: Methods and systems are provided for determining scan settings for a localizer scan based on a magnetic resonance (MR) calibration image. In one example, a method for magnetic resonance imaging (MRI) includes acquiring an MR calibration image of an imaging subject, mapping, by a trained deep neural network, the MR calibration image to a corresponding anatomical region of interest (ROI) attribute map for an anatomical ROI of the imaging subject, adjusting one or more localizer scan parameters based on the anatomical ROI attribute map, and acquiring one or more localizer images of the anatomical ROI according to the one or more localizer scan parameters.

公开(公告)号：US20250131570A1

公开(公告)日：2025-04-24

申请号：US18491985

申请日：2023-10-23

Applicant: GE Precision Healthcare LLC

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

IPC: G06T7/11 ,  A61B5/055 ,  G01R33/48 ,  G01R33/56 ,  G01R33/58 ,  G06T7/73 ,  G06V10/82 ,  G06V20/50 ,  G06V20/70 ,  G16H30/40

Abstract: A method and a system include obtaining calibration scan data or low resolution images of a subject acquired with a magnetic resonance (MR) scanner of an MR imaging system. The method and the system also include inputting the calibration data or the low resolution images into a trained deep learning-based multi-mask segmentation network. The method and the system further include outputting labeled mask images for different anatomical stations, wherein a respective mask of a respective labeled mask image highlights an anatomical landmark of interest in each respective anatomical station of the different anatomical stations. The method and the system even further include determining an extent of a respective anatomical landmark of interest in each respective anatomical station of the different anatomical stations for a respective localizer scan for each respective anatomical station based at least on a respective label mask image for each respective anatomical station.

公开(公告)号：US20250114008A1

公开(公告)日：2025-04-10

申请号：US18483757

申请日：2023-10-10

Applicant: GE Precision Healthcare LLC

Inventor： Dattesh Dayanand Shanbhag ,  Kavitha Manickam ,  Dawei Gui ,  Maggie MeiKei Fung ,  Ting Ye ,  Chitresh Bhushan ,  Muhan Shao

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

Abstract: A method for performing a scan of a subject includes receiving a selected protocol for the scan and triggering, upon receiving a start signal, automatic landmarking of the subject on a table of a magnetic resonance imaging (MRI) scanner utilizing a three-dimensional (3D) camera. The method includes obtaining landmark positioning data from the 3D camera and utilizing the landmark positioning data for localization of the region of interest. The method includes, subsequent to the automatic landmarking, triggering a calibration scan of the subject with the MRI scanner and obtaining calibration data from the MRI scanner and utilizing the calibration data for refining the localization of the region of interest. The method includes generating a geometry plan for subsequent scans utilizing both the landmark positioning data and the calibration data and triggering at least one subsequent scan of the subject with the MRI scanner based on the geometry plan.

公开(公告)号：US12106478B2

公开(公告)日：2024-10-01

申请号：US17203196

申请日：2021-03-16

Applicant: GE Precision Healthcare LLC

Inventor： Florintina C. ,  Deepa Anand ,  Dattesh Dayanand Shanbhag ,  Chitresh Bhushan ,  Radhika Madhavan

IPC: G06T7/00 ,  G06N20/00 ,  G06T3/147 ,  G06T7/11

CPC classification number: G06T7/0014 ,  G06N20/00 ,  G06T3/147 ,  G06T7/11 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30008

Abstract: A medical imaging system includes at least one medical imaging device providing image data of a subject and a processing system programmed to generate a plurality of training images having simulated medical conditions by blending a pathology region from a plurality of template source images to a plurality of target images. The processing system is further programmed to train a deep learning network model using the plurality of training images and input the image data of the subject to the deep learning network model. The processing system is further programmed to generate a medical image of the subject based on the output of the deep learning network model.

公开(公告)号：US20240005480A1

公开(公告)日：2024-01-04

申请号：US17810473

申请日：2022-07-01

Applicant: GE Precision Healthcare LLC

Inventor： Chitresh Bhushan ,  Dattesh Dayanand Shanbhag ,  Soumya Ghose ,  Amod Suhas Jog

IPC: G06T7/00 ,  G16H30/40

CPC classification number: G06T7/0012 ,  G16H30/40 ,  G06T2207/20084 ,  G06T2207/20081

Abstract: Methods and systems are provided for automatic placement of at least one saturation band on a medical image, which may direct saturation pulses during a MRI scan. A method may include acquiring a localizer image of an imaging subject, determining a plane mask for the localizer image by entering the localizer image as input to a deep neural network trained to output the plane mask based on the localizer image, generating a saturation band based on the plane mask by positioning the saturation band at a position and an angulation of the plane mask, and outputting a graphical prescription for display on a display device, the graphical prescription including the saturation band overlaid on the medical image.