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：US20220335597A1

公开(公告)日：2022-10-20

申请号：US17233807

申请日：2021-04-19

Applicant: GE Precision Healthcare LLC

Inventor： Dattesh Shanbhag ,  Deepa Anand ,  Chitresh Bhushan ,  Arathi Sreekumari ,  Soumya Ghose

IPC: G06T7/00 ,  G06N20/00

Abstract: Systems and methods for workflow management for labeling the subject anatomy are provided. The method comprises obtaining at least one localizer image of a subject anatomy using a low-resolution medical imaging device. The method further comprises labeling at least one anatomical point within the at least one localizer image. The method further comprises extracting using a machine learning module a mask of the at least one localizer image comprising the at least one anatomical point label. The method further comprises using the mask to label at least one anatomical point on a high-resolution image of the subject anatomy based on the at least one anatomical point within the localizer image.

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：US12039007B2

公开(公告)日：2024-07-16

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: G06F18/214 ,  G06F18/211 ,  G06F18/22 ,  G06F18/232 ,  G06N3/08 ,  G16H30/40

CPC classification number: G06F18/2148 ,  G06F18/211 ,  G06F18/2155 ,  G06F18/22 ,  G06F18/232 ,  G06N3/08 ,  G16H30/40 ,  G06V2201/03

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：US20240215848A1

公开(公告)日：2024-07-04

申请号：US18090131

申请日：2022-12-28

Applicant: GE Precision Healthcare LLC

Inventor： Florian Wiesinger ,  Dattesh Dayanand Shanbhag ,  Kavitha Manickam ,  Harsh Kumar Agarwal ,  Dawei Gui ,  Chitresh Bhushan

IPC: A61B5/055 ,  G01R33/58

CPC classification number: A61B5/055 ,  G01R33/58 ,  G01R33/543

Abstract: A method for performing a scan of a subject utilizing a magnetic resonance imaging (MRI) system includes triggering a prescan by an MRI scanner of the MRI system upon the subject being setup on a table of the MRI scanner and the table reaching an iso-center of the MRI scanner. The method includes subsequent to the prescan, triggering a calibration scan of the subject with the MRI scanner, wherein the calibration scan is an acoustic noise suppressed MRI scan. The method includes obtaining calibration data from the calibration scan. The method includes obtaining prescription parameters for subsequent scans of the subject with the MRI scanner from the calibration data. The method includes triggering at least one scan of the subject with the MRI scanner based on the prescription parameters.

公开(公告)号：US11978137B2

公开(公告)日：2024-05-07

申请号：US18343258

申请日：2023-06-28

Applicant: GE Precision Healthcare LLC

Inventor： Chitresh Bhushan ,  Dattesh Dayanand Shanbhag ,  Rakesh Mullick

IPC: G06T11/00 ,  A61B5/00 ,  A61B5/055 ,  G05F1/46 ,  G06F1/26 ,  G06F13/16 ,  G06F13/42 ,  G06T7/00 ,  G06T7/11 ,  G06T7/80 ,  G06V10/25 ,  H03F3/45 ,  H03K5/1252

CPC classification number: G06T11/008 ,  A61B5/055 ,  A61B5/742 ,  G05F1/46 ,  G06F1/266 ,  G06F13/1668 ,  G06F13/4282 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/80 ,  G06V10/25 ,  H03F3/45475 ,  H03K5/1252 ,  G06F2213/0026 ,  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.

公开(公告)号：US20240138697A1

公开(公告)日：2024-05-02

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

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：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：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：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.