公开(公告)号：US20230260142A1

公开(公告)日：2023-08-17

申请号：US17648696

申请日：2022-01-24

Applicant: GE Precision Healthcare LLC

Inventor： Sudhanya Chatterjee ,  Dattesh Dayanand Shanbhag ,  Krishna Seetharam Shriram

IPC: G06T7/33

CPC classification number: G06T7/344 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Systems/techniques that facilitate multi-modal image registration via modality-neutral machine learning transformation are provided. In various embodiments, a system can access a first image and a second image, where the first image can depict an anatomical structure according to a first imaging modality, and where the second image can depict the anatomical structure according to a second imaging modality that is different from the first imaging modality. In various aspects, the system can generate, via execution of a machine learning model on the first image and the second image, a modality-neutral version of the first image and a modality-neutral version of the second image. In various instances, the system can register the first image with the second image, based on the modality-neutral version of the first image and the modality-neutral version of the second image.

公开(公告)号：US20220375035A1

公开(公告)日：2022-11-24

申请号：US17325010

申请日：2021-05-19

Applicant: GE Precision Healthcare LLC

Inventor： Sudhanya Chatterjee ,  Dattesh Dayanand Shanbhag

IPC: G06T5/00 ,  G06T7/00 ,  G06N3/08

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：US20220351055A1

公开(公告)日：2022-11-03

申请号：US17243046

申请日：2021-04-28

Applicant: GE Precision Healthcare LLC

Inventor： Deepa Anand ,  Rakesh Mullick ,  Dattesh Dayanand Shanbhag ,  Marc T. Edgar

IPC: G06N5/04 ,  G06N20/00

Abstract: Systems and techniques that facilitate data diversity visualization and/or quantification for machine learning models are provided. In various embodiments, a processor can access a first dataset and a second dataset, where a machine learning (ML) model is trained on the first dataset. In various instances, the processor can obtain a first set of latent activations generated by the ML model based on the first dataset, and a second set of latent activations generated by the ML model based on the second dataset. In various aspects, the processor can generate a first set of compressed data points based on the first set of latent activations, and a second set of compressed data points based on the second set of latent activations, via dimensionality reduction. In various instances, a diversity component can compute a diversity score based on the first set of compressed data points and second set of compressed data points.

公开(公告)号：US11133100B2

公开(公告)日：2021-09-28

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 ,  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

公开(公告)号：US20250123346A1

公开(公告)日：2025-04-17

申请号：US18484860

申请日：2023-10-11

Applicant: GE Precision Healthcare LLC

Inventor： Tisha Anie Abraham ,  Dattesh Dayanand Shanbhag ,  Harsh Kumar Agarwal ,  Sheila Srinivasan Washburn ,  Maggie MeiKei Fung ,  Suchandrima Banerjee ,  Patrick Quarterman ,  Ramesh Venkatesan ,  Sajith Rajamani

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

Abstract: A method includes receiving a selection of a scan protocol for the scan of a subject and obtaining localizer images including an anatomic landmark of interest of the subject acquired with the MRI system. The method includes automatically detecting the anatomic landmark of interest in localizer images and determining a geometry plan of the scan including extents of the anatomic landmark of interest. The method includes automatically determining a coverage of the scan to include the anatomic landmark of interest and to match the extents of the anatomic landmark of interest. The method includes obtaining limits on adjustments scan time and one or more image quality parameters for the scan protocol. The method includes generating an updated scan protocol by automatically adjusting one or more parameters of the scan protocol based on the scan protocol, the limits on adjustments, and the coverage of the scan.

公开(公告)号：US20250029316A1

公开(公告)日：2025-01-23

申请号：US18356083

申请日：2023-07-20

Applicant: GE Precision Healthcare LLC

Inventor： Rohan Keshav Patil ,  Sudhanya Chatterjee ,  Dattesh Dayanand Shanbhag

IPC: G06T15/08 ,  G06T3/40 ,  G06T7/11

Abstract: The disclosure relates to multiplanar reformation of three-dimensional medical images. In particular, the invention provides a method for reformatting image sequences by determining a landmark plane intersecting a volume, acquiring an image sequence, reformatting the image sequence along the landmark plane to produce a first reformatted image sequence, perturbing the landmark plane to produce a perturbed landmark plane, reformatting the first reformatted image sequence along the perturbed landmark plane to produce a second reformatted image sequence, mapping the second reformatted image sequence, the image sequence, and the landmark plane, to a resolution enhanced image sequence using a trained image enhancement network, and displaying the resolution enhanced image sequence via a display device. The present disclosure provides approaches which may reduce image artifacts in retrospectively reformatted image sequences, particularly in cases of retrospective reformatting of medium or low-resolution image sequences, without relying on acquisition of high-resolution 3D images.

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.