公开(公告)号：US12276715B2

公开(公告)日：2025-04-15

申请号：US18331885

申请日：2023-06-08

Applicant: GE Precision Healthcare LLC

Inventor： Sudhanya Chatterjee ,  Megha Goel ,  Suresh Emmanuel Devadoss Joel ,  Rohan Keshav Patil ,  Florintina C ,  Preetham Shankapal

IPC: G01R33/48 ,  A61B5/055 ,  G01R33/56 ,  G01R33/565 ,  G06T7/00

Abstract: Systems and methods are provided for reconstructing images from motion-affected k-space data. In one example, a method comprises obtaining k-space data of a spin echo magnetic resonance imaging (MRI) exam of a subject, the k-space data comprising a plurality of echo train lengths (ETLs), with each ETL comprising a subset of lines of the k-space data. The method further comprises identifying a subset of ETLs of the plurality of ETLs of the k-space data corresponding to a dominant pose of the subject, generating an undersampled version of the k-space data, the undersampled version including only the subset of ETLs, entering the undersampled version of the k-space data as input to a reconstruction model trained to output a reconstructed image based on the undersampled version of the k-space data, and displaying the reconstructed image on a display device and/or saving the reconstructed image in memory.

公开(公告)号：US11763429B2

公开(公告)日：2023-09-19

申请号：US17325010

申请日：2021-05-19

Applicant: GE Precision Healthcare LLC

Inventor： Sudhanya Chatterjee ,  Dattesh Dayanand Shanbhag

IPC: G06K9/00 ,  G06T5/00 ,  G06T7/00 ,  G06N3/08

CPC classification number: G06T5/002 ,  G06N3/08 ,  G06T7/0002 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084

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：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：US20240410965A1

公开(公告)日：2024-12-12

申请号：US18331885

申请日：2023-06-08

Applicant: GE Precision Healthcare LLC

Inventor： Sudhanya Chatterjee ,  Megha Goel ,  Suresh Emmanuel Devadoss Joel ,  Rohan Keshav Patil ,  Florintina C ,  Preetham Shankapal

IPC: G01R33/48 ,  A61B5/055 ,  G01R33/56 ,  G01R33/565 ,  G06T7/00

Abstract: Systems and methods are provided for reconstructing images from motion-affected k-space data. In one example, a method comprises obtaining k-space data of a spin echo magnetic resonance imaging (MRI) exam of a subject, the k-space data comprising a plurality of echo train lengths (ETLs), with each ETL comprising a subset of lines of the k-space data. The method further comprises identifying a subset of ETLs of the plurality of ETLs of the k-space data corresponding to a dominant pose of the subject, generating an undersampled version of the k-space data, the undersampled version including only the subset of ETLs, entering the undersampled version of the k-space data as input to a reconstruction model trained to output a reconstructed image based on the undersampled version of the k-space data, and displaying the reconstructed image on a display device and/or saving the reconstructed image in memory.

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：US20250157098A1

公开(公告)日：2025-05-15

申请号：US18506457

申请日：2023-11-10

Applicant: GE Precision Healthcare LLC

Inventor： Florintina C ,  Sudhanya Chatterjee ,  Rohan Patil ,  Suresh Emmanuel Devadoss Joel

IPC: G06T11/00 ,  G01R33/48 ,  G01R33/56

Abstract: A system and method for reducing scan time of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging include acquiring a plurality of blades of k-space data of a region of interest in a rotational manner around a center of k-space via a magnetic resonance imaging (MRI) scanner during a PROPELLER sequence, wherein each blade of the plurality of blades of k-space data includes a plurality of parallel phase encoding lines sampled in a phase encoding order. Each blade of the plurality of blades of k-space data is undersampled. The system and method include utilizing a deep learning-based Cartesian-like reconstruction network to individually and separately reconstruct each blade of the plurality of blades of k-space data to generate a plurality of fully sampled blades. The system and method include utilizing a PROPELLER reconstruction algorithm to generate a complex image from the plurality of fully sampled blades.

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：US20240312004A1

公开(公告)日：2024-09-19

申请号：US18182998

申请日：2023-03-13

Applicant: GE Precision Healthcare LLC

Inventor： Sudhanya Chatterjee ,  Dattesh Shanbhag ,  Rakesh Mullick ,  Aanchal Mongia

IPC: G06T7/00 ,  A61B5/055 ,  G06T5/00 ,  G06V10/70

CPC classification number: G06T7/0012 ,  A61B5/055 ,  G06T5/70 ,  G06V10/70 ,  G06T2207/10088 ,  G06T2207/30096

Abstract: Various methods and systems are provided for reducing parametric heterogeneity in quantitative magnetic resonance (qMR) images, to increase robustness of in-field machine learning model inferences. In one example, a method for reducing qMR image heterogeneity includes, receiving a first qMR image, acquired using a first value of an acquisition parameter, determining a target value of the acquisition parameter based on a training dataset of a machine learning model, generating a synthetic qMR image, wherein the synthetic qMR image simulates a qMR image acquired using the target value of the acquisition parameter, by mapping the first qMR image to the synthetic qMR image using an analytical model, and feeding the synthetic qMR image to the machine learning model.

公开(公告)号：US20240005451A1

公开(公告)日：2024-01-04

申请号：US17810271

申请日：2022-06-30

Applicant: GE Precision Healthcare LLC

Inventor： Rohan Keshav Patil ,  Sudhanya Chatterjee

IPC: G06T3/40 ,  G16H30/40

CPC classification number: G06T3/4076 ,  G06T3/4046 ,  G16H30/40

Abstract: Various methods and systems are provided for generating super-resolution images. In one embodiment, a method comprises: progressively up-sampling an input image to generate a super-resolution output image by: generating N intermediate images based on the input image, where N is equal to at least one, including a first intermediate image by providing the input image to a deep neural network, where a resolution of the first intermediate image is a multiple of a resolution of the input image, higher than the resolution of the input image, and can be any positive real value and not necessarily an integer value; generating the super-resolution output image based on the N intermediate images, the super-resolution output image having a resolution higher than a respective resolution of each intermediate image of the N intermediate images and the resolution of the input image; and displaying the super-resolution output image via a display device.

公开(公告)号：US20240385267A1

公开(公告)日：2024-11-21

申请号：US18665381

申请日：2024-05-15

Applicant: GE Precision Healthcare LLC

Inventor： Sudhanya Chatterjee ,  Harsh Kumar Agarwal ,  Florintina C ,  Rohan Keshav Patil ,  Suresh Emmanuel Devadoss Joel ,  Sajith Rajamani

IPC: G01R33/48 ,  G01R33/385 ,  G01R33/56

Abstract: A method for magnetic resonance imaging (MRI) includes determining a Partial Fourier (PF) factor and an acceleration factor for acquiring k-space data from a subject. The method also includes acquiring a set of k-space data from the subject using the PF factor along with an under-sampling technique, wherein the under-sampling technique is dependent on the acceleration factor. The image of the subject is reconstructed by processing the set of k-space data using a deep learning (DL) network.