公开(公告)号：US11885862B2

公开(公告)日：2024-01-30

申请号：US17083074

申请日：2020-10-28

Applicant: GE Precision Healthcare LLC

Inventor： Sudhanya Chatterjee ,  Dattesh Shanbhag ,  Suresh Joel

IPC: G01R33/56 ,  A61B5/055 ,  G06T7/00 ,  G01R33/48 ,  G01R33/483 ,  G06F18/22

CPC classification number: G01R33/5608 ,  A61B5/055 ,  G01R33/4818 ,  G01R33/4835 ,  G06F18/22 ,  G06T7/0012 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Systems and methods for deep learning based magnetic resonance imaging (MRI) examination acceleration are provided. The method of deep learning (DL) based magnetic resonance imaging (MRI) examination acceleration comprises acquiring at least one fully sampled reference k-space data of a subject and acquiring a plurality of partial k-space of the subject. The method further comprises grafting the plurality of partial k-space with the at least one fully sampled reference k-space data to generate a grafted k-space for accelerated examination. The method further comprises training a deep learning (DL) module using the fully sampled reference k-space data and the grafted k-space to remove the grafting artifacts.

公开(公告)号：US11808832B2

公开(公告)日：2023-11-07

申请号：US17344274

申请日：2021-06-10

Applicant: GE PRECISION HEALTHCARE LLC

Inventor： Sudhanya Chatterjee ,  Dattesh Dayanand Shanbhag

IPC: G06T7/00 ,  G01R33/565 ,  G06N3/084

CPC classification number: G01R33/56554 ,  G06N3/084 ,  G06T7/0012 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084

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：US20250052843A1

公开(公告)日：2025-02-13

申请号：US18446898

申请日：2023-08-09

Applicant: GE Precision Healthcare LLC

Inventor： Florintina C ,  Suresh Emmanuel Devadoss Joel ,  Sajith Rajamani ,  Preetham Shankpal ,  Megha Goel ,  Sudhanya Chatterjee

IPC: G01R33/565 ,  G01R33/48 ,  G01R33/56 ,  G06T5/00

Abstract: A system and method for improving image quality 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 from a coil 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. The system and method also include utilizing a deep learning-based denoising network to denoise each blade of the plurality of blades of k-space data to generate a plurality of denoised blades. The system and method further include utilizing a PROPELLER reconstruction algorithm to generate a complex image from the plurality of denoised blades.

公开(公告)号：US20240378696A1

公开(公告)日：2024-11-14

申请号：US18144452

申请日：2023-05-08

Applicant: GE Precision Healthcare LLC

Inventor： Florintina C ,  Sajith Rajamani ,  Preetham Shankpal ,  Suresh Emmanuel Devadoss Joel ,  Sudhanya Chatterjee ,  Rohan Patil ,  Ramesh Venkatesan ,  Rajagopalan Sundaresan ,  Harsh Kumar Agarwal

IPC: G06T5/50 ,  G06T5/00

Abstract: A method includes acquiring an MRI complex signal having a plurality of complex echoes during an SWI sequence. The method includes phase filtering each complex echo of the plurality of complex echoes. The method also includes generating a respective phase image and a respective magnitude image from each phase filtered complex echo. The method further includes combining separately the respective magnitude images of the plurality of complex echoes with each other to generate a combined magnitude image and the respective phase images of the plurality of complex echoes with each other to generate a combined phase image. The method includes generating a complex image from both the combined magnitude image and the combined phase image. The method includes utilizing a deep learning-based denoising network to denoise the complex image to generate a denoised complex image.

公开(公告)号：US20230342913A1

公开(公告)日：2023-10-26

申请号：US17660717

申请日：2022-04-26

Applicant: GE Precision Healthcare LLC

Inventor： Mahendra Madhukar Patil ,  Rakesh Mullick ,  Sudhanya Chatterjee ,  Syed Asad Hashmi ,  Dattesh Dayanand Shanbhag ,  Deepa Anand ,  Suresh Emmanuel Devadoss Joel

IPC: G06T7/00 ,  G06N20/00 ,  G06V10/25

CPC classification number: G06T7/0012 ,  G06N20/00 ,  G06V10/25 ,  G06V2201/03

Abstract: Techniques are described for generating high quality training data collections for training artificial intelligence (AI) models in the medical imaging domain. A method embodiment comprises receiving, by a system comprising processor, input indicating a clinical context associated with usage of a medical image dataset, and selecting, by the system, one or more data scrutiny metrics for filtering the medical image dataset based on the clinical context. The method further comprises applying, by the system, one or more image processing functions to the medical image dataset to generate metric values of the one or more data scrutiny metrics for respective medical images included in the medical image dataset, filtering, by the system, the medical image dataset into one or more subsets based on one or more acceptability criteria for the metric values.

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：US20220128640A1

公开(公告)日：2022-04-28

申请号：US17083074

申请日：2020-10-28

Applicant: GE Precision Healthcare LLC

Inventor： Sudhanya Chatterjee ,  Dattesh Shanbhag ,  Suresh Joel

IPC: G01R33/56 ,  A61B5/055 ,  G06T7/00 ,  G01R33/48 ,  G01R33/483 ,  G06K9/62

Abstract: Systems and methods for deep learning based magnetic resonance imaging (MRI) examination acceleration are provided. The method of deep learning (DL) based magnetic resonance imaging (MRI) examination acceleration comprises acquiring at least one fully sampled reference k-space data of a subject and acquiring a plurality of partial k-space of the subject. The method further comprises grafting the plurality of partial k-space with the at least one fully sampled reference k-space data to generate a grafted k-space for accelerated examination. The method further comprises training a deep learning (DL) module using the fully sampled reference k-space data and the grafted k-space to remove the grafting artifacts.