公开(公告)号：US20230004872A1

公开(公告)日：2023-01-05

申请号：US17365650

申请日：2021-07-01

Applicant: GE PRECISION HEALTHCARE LLC

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

IPC: G06N20/20 ,  G06K9/62 ,  G06T7/00

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：US20220397627A1

公开(公告)日：2022-12-15

申请号：US17344274

申请日：2021-06-10

Applicant: GE PRECISION HEALTHCARE LLC

Inventor： Sudhanya Chatterjee ,  Dattesh Dayanand Shanbhag

IPC: G01R33/565 ,  G06N3/08 ,  G06T7/00

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：US20220301163A1

公开(公告)日：2022-09-22

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 ,  G06T7/11 ,  G06T3/00

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：US11195277B2

公开(公告)日：2021-12-07

申请号：US16457710

申请日：2019-06-28

Applicant: GE Precision Healthcare LLC

Inventor： Dattesh Dayanand Shanbhag ,  Arathi Sreekumari ,  Sandeep Kaushik

IPC: G06T7/00 ,  G06T11/00

Abstract: Methods and systems are provided for generating a normative medical image from an anomalous medical image. In an example, the method includes receiving an anomalous medical image, wherein the anomalous medical image includes anomalous data, mapping the anomalous medical image to a normative medical image using a trained generative network of a generative adversarial network (GAN), wherein the anomalous data of the anomalous medical image is mapped to normative data in the normative medical image. In some examples, the method may further include displaying the normative medical image via a display device, and/or utilizing the normative medical image for further image analysis tasks to generate robust outcomes from the anomalous medical image.

公开(公告)号：US20210158935A1

公开(公告)日：2021-05-27

申请号：US16691430

申请日：2019-11-21

Applicant: GE Precision Healthcare LLC

Inventor： Hariharan Ravishankar ,  Dattesh Dayanand Shanbhag

IPC: G16H30/40 ,  G16H30/20 ,  A61B5/055 ,  A61B6/03 ,  G06T7/00 ,  G06T11/00 ,  G06N3/04

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：US10799204B2

公开(公告)日：2020-10-13

申请号：US15306616

申请日：2015-04-24

Applicant: GE Precision Healthcare LLC

Inventor： Dattesh Dayanand Shanbhag ,  Venkata Veerendranadh Chebrolu

IPC: G06K9/00 ,  A61B6/00 ,  A61B5/055 ,  G01R33/565 ,  A61B5/00 ,  G06T7/00 ,  A61B6/03 ,  A61B8/08 ,  G01R33/56 ,  G06K9/46 ,  G06K9/62

Abstract: A method for automated evaluation of motion correction is presented. The method includes identifying one or more regions of interest in each of a plurality of images corresponding to a subject of interest. Furthermore, the method includes selecting valid voxels in each of the one or more regions of interest in each of the plurality of images. The method also includes computing a similarity metric, a dispersion metric, or both the similarity metric and the dispersion metric for each region of interest in each of the plurality of images. Additionally, the method includes generating a similarity map, a dispersion map, or both the similarity map and the dispersion map based on the similarity metrics and the dispersion metrics corresponding to the one or more regions of interest.

公开(公告)号：US20250104221A1

公开(公告)日：2025-03-27

申请号：US18491992

申请日：2023-10-23

Applicant: GE Precision Healthcare LLC

Inventor： Dattesh Dayanand Shanbhag ,  Deepa Anand ,  Rakesh Mullick ,  Sudhanya Chatterjee ,  Aanchal Mongia ,  Uday Damodar Patil

IPC: G06T7/00 ,  G06T5/40 ,  G06V10/44 ,  G06V20/70 ,  G16H15/00 ,  G16H30/20 ,  G16H30/40

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：US20240280654A1

公开(公告)日：2024-08-22

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：US12048521B2

公开(公告)日：2024-07-30

申请号：US17973855

申请日：2022-10-26

Applicant: GE Precision Healthcare LLC

Inventor： Dattesh Dayanand Shanbhag ,  Chitresh Bhushan ,  Deepa Anand ,  Kavitha Manickam ,  Dawei Gui ,  Radhika Madhavan

IPC: A61B5/055 ,  G01R33/20 ,  G01R33/56

CPC classification number: A61B5/055 ,  G01R33/20 ,  G01R33/5608

Abstract: A method for generating an image of a subject with a magnetic resonance imaging (MRI) system is presented. The method includes first performing a localizer scan of the subject to acquire localizer scan data. A machine learning (ML) module is then used to detect the presence of metal regions in the localizer scan data based on magnitude and phase information of the localizer scan data. Based on the detected metal regions in the localizer scan data, the MRI workflow is adjusted for diagnostic scan of the subject. The image of the subject is then generated using the adjusted workflow.

公开(公告)号：US11776173B2

公开(公告)日：2023-10-03

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 ,  G06T7/80 ,  G06T7/11 ,  G06V10/25 ,  A61B5/055 ,  A61B5/00 ,  G06T7/00

CPC classification number: G06T11/008 ,  A61B5/055 ,  A61B5/742 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/80 ,  G06V10/25 ,  G06T2207/10072 ,  G06T2207/20081 ,  G06T2207/30016

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.