公开(公告)号：US20230293014A1

公开(公告)日：2023-09-21

申请号：US18142726

申请日：2023-05-03

Applicant: GE Precision Healthcare LLC

Inventor： Dattesh Dayanand Shanbhag ,  Rekesh Mullick ,  Arathi Sreekumari ,  Uday Damodar Patil ,  Trevor John Kolupar ,  Chitresh Bhushan ,  Andre de Almeida Maximo ,  Thomas Kwok-Fah Foo ,  Maggie MeiKei Fung

IPC: A61B5/00 ,  A61B5/055

CPC classification number: A61B5/0033 ,  A61B5/055 ,  G06T7/0012

Abstract: The present disclosure relates to use of a workflow for automatic prescription of different radiological imaging scan planes across different anatomies and modalities. The automated prescription of such imaging scan planes helps ensure contiguous visualization of the different landmark structures. Unlike prior approaches, the disclosed technique determines the necessary planes using the localizer images itself and does not explicitly segment or delineate the landmark structures to perform plane prescription.

公开(公告)号：US11610313B2

公开(公告)日：2023-03-21

申请号：US17512469

申请日：2021-10-27

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：US11506739B2

公开(公告)日：2022-11-22

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: G01V3/00 ,  G01R33/58 ,  G01R33/54 ,  A61B5/055 ,  G06F9/54 ,  G06N3/08 ,  G06N20/00 ,  A61B5/00

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：US20210327566A1

公开(公告)日：2021-10-21

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 ,  G06N3/04 ,  G06T7/00 ,  G06T11/00

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