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：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：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.

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：US20250149169A1

公开(公告)日：2025-05-08

申请号：US18504649

申请日：2023-11-08

Applicant: GE Precision Healthcare LLC

Inventor： Deepa Anand ,  Dattesh Shanbhag ,  Hariharan Ravishankar ,  Suresh Emmanuel Devadoss Joel ,  Rakesh Mullick ,  Rachana Sathish ,  Rahul Venkataramani ,  Krishna Seetharam Shriram ,  Prasad Sudhakara Murthy

IPC: G16H50/20 ,  G06T7/00 ,  G16H30/40

Abstract: Systems or techniques for facilitating learnable visual prompt engineering are provided. In various embodiments, a system can access a medical image and a pre-trained machine learning model that is configured to perform a diagnostic or prognostic inferencing task. In various aspects, the system can apply a pre-processing transformation to one or more pixels or voxels of the medical image, thereby yielding a transformed version of the medical image, wherein the pre-processing transformation can convert an input pixel or voxel intensity value to an output pixel or voxel intensity value via one or more parameters that are iteratively learned. In various instances, the system can perform the diagnostic or prognostic inferencing task, by executing the pre-trained machine learning model on the transformed version of the medical 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.