公开(公告)号：US20230029188A1

公开(公告)日：2023-01-26

申请号：US17385600

申请日：2021-07-26

Applicant: GE Precision Healthcare LLC

Inventor： Rajesh Langoju ,  Utkarsh Agrawal ,  Bhushan Patil ,  Vanika Singhal ,  Bipul Das ,  Jiang Hsieh

IPC: G06T5/00 ,  G06T7/00 ,  G06N3/08 ,  G06K9/62

Abstract: The current disclosure provides methods and systems to reduce an amount of structured and unstructured noise in image data. Specifically, a multi-stage deep learning method is provided, comprising training a deep learning network using a set of training pairs interchangeably including input data from a first noisy dataset with a first noise level and target data from a second noisy dataset with a second noise level, and input data from the second noisy dataset and target data from the first noisy dataset; generating an ultra-low noise data equivalent based on a low noise data fed into the trained deep learning network; and retraining the deep learning network on the set of training pairs using the target data of the set of training pairs in a first retraining step, and using the ultra-low noise data equivalent as target data in a second retraining step.

公开(公告)号：US20230018833A1

公开(公告)日：2023-01-19

申请号：US17379003

申请日：2021-07-19

Applicant: GE Precision Healthcare LLC

Inventor： Bipul Das ,  Rakesh Mullick ,  Utkarsh Agrawal ,  KS Shriram ,  Sohan Ranjan ,  Tao Tan

IPC: G16H50/20 ,  G06N5/04

Abstract: Techniques are described for generating multimodal training data cohorts tailored to specific clinical machine learning (ML) model inferencing tasks. In an embodiment, a method comprises accessing, by a system comprising a processor, multimodal clinical data for a plurality of subjects included in one or more clinical data sources. The method further comprises selecting, by the system, datasets from the multimodal clinical data based on the datasets respectively comprising subsets of the multimodal clinical data that satisfy criteria determined to be relevant to a clinical processing task. The method further comprises generating, by the system, a training data cohort comprising the datasets for training a clinical inferencing model to perform the clinical processing task.

公开(公告)号：US20250118062A1

公开(公告)日：2025-04-10

申请号：US18482085

申请日：2023-10-06

Applicant: GE Precision Healthcare LLC

Inventor： Utkarsh Agrawal ,  Bipul Das ,  Prasad Sudhakara Murthy

IPC: G06V10/82 ,  G06V10/77 ,  G16H30/20

Abstract: Systems or techniques that facilitate explainable visual attention for deep learning are provided. In various embodiments, a system can access a medical image generated by a medical imaging scanner. In various aspects, the system can perform, via execution of a deep learning neural network, an inferencing task on the medical image. In various instances, the deep learning neural network can receive as input the medical image and can produce as output both an inferencing task result and an attention map indicating on which pixels or voxels of the medical image the deep learning neural network focused in generating the inferencing task result.

公开(公告)号：US20250049400A1

公开(公告)日：2025-02-13

申请号：US18929269

申请日：2024-10-28

Applicant: GE Precision Healthcare LLC

Inventor： Rajesh Langoju ,  Utkarsh Agrawal ,  Risa Shigemasa ,  Bipul Das ,  Yasuhiro Imai ,  Jiang Hsieh

IPC: A61B6/02 ,  G06N3/04 ,  G06N3/08 ,  G06T3/4076 ,  G06T11/00 ,  G06T15/08

Abstract: Various methods and systems are provided for computed tomography imaging. In one embodiment, a method includes acquiring, with an x-ray detector and an x-ray source coupled to a gantry, a three-dimensional image volume of a subject while the subject moves through a bore of the gantry and the gantry rotates the x-ray detector and the x-ray source around the subject, inputting the three-dimensional image volume to a trained deep neural network to generate a corrected three-dimensional image volume with a reduction in aliasing artifacts present in the three-dimensional image volume, and outputting the corrected three-dimensional image volume. In this way, aliasing artifacts caused by sub-sampling may be removed from computed tomography images while preserving details, texture, and sharpness in the computed tomography images.

公开(公告)号：US12131446B2

公开(公告)日：2024-10-29

申请号：US17368534

申请日：2021-07-06

Applicant: GE Precision Healthcare LLC

Inventor： Rajesh Veera Venkata Lakshmi Langoju ,  Prasad Sudhakara Murthy ,  Utkarsh Agrawal ,  Bhushan D. Patil ,  Bipul Das

IPC: G06T5/73 ,  G06N20/00 ,  G06T3/4053 ,  G06T5/20

CPC classification number: G06T5/73 ,  G06N20/00 ,  G06T3/4053 ,  G06T5/20 ,  G06T2207/20081

Abstract: Systems/techniques that facilitate self-supervised deblurring are provided. In various embodiments, a system can access an input image generated by an imaging device. In various aspects, the system can train, in a self-supervised manner based on a point spread function of the imaging device, a machine learning model to deblur the input image. More specifically, the system can append to the model one or more non-trainable convolution layers having a blur kernel that is based on the point spread function of the imaging device. In various aspects, the system can feed the input image to the model, the model can generate a first output image based on the input image, the one or more non-trainable convolution layers can generate a second output image by convolving the first output image with the blur kernel, and the system can update parameters of the model based on a difference between the input image and the second output image.

公开(公告)号：US20240265591A1

公开(公告)日：2024-08-08

申请号：US18164372

申请日：2023-02-03

Applicant: GE Precision Healthcare LLC

Inventor： Bipul Das ,  Utkarsh Agrawal ,  Prasad Sudhakara Murthy ,  Risa Shigemasa ,  Kentaro Ogata ,  Yasuhiro Imai

IPC: G06T11/00 ,  G06T3/40 ,  G06V10/44 ,  G06V10/80

CPC classification number: G06T11/005 ,  G06T3/4053 ,  G06V10/44 ,  G06V10/806 ,  G06T2207/10081 ,  G06T2207/10116 ,  G06T2211/408

Abstract: Methods and systems are provided for interpolating missing views in dual-energy computed tomography data. In one example, a method includes obtaining a first sinogram missing a plurality of views and a second sinogram missing a different plurality of views, the first sinogram acquired with a first X-ray source energy during a scan and the second sinogram acquired with a second, different X-ray source energy during the scan; initializing each of the first sinogram and the second sinogram to form a first initialized sinogram and a second initialized sinogram; entering the first initialized sinogram and the second initialized sinogram into the same or different interpolation models trained to output a first filled sinogram based on the first initialized sinogram and output a second filled sinogram based on the second initialized sinogram; and reconstructing one or more images from the first filled sinogram and the second filled sinogram.

公开(公告)号：US20240062331A1

公开(公告)日：2024-02-22

申请号：US17821058

申请日：2022-08-19

Applicant: GE Precision Healthcare LLC

Inventor： Rajesh Langoju ,  Prasad Sudhakara Murthy ,  Utkarsh Agrawal ,  Risa Shigemasa ,  Bhushan Patil ,  Bipul Das ,  Yasuhiro Imai

IPC: G06T3/40 ,  G06T7/00 ,  G06T5/00 ,  G06T7/11 ,  G06N3/08

CPC classification number: G06T3/4046 ,  G06T7/0012 ,  G06T5/002 ,  G06T7/11 ,  G06N3/08

Abstract: Systems/techniques that facilitate deep learning robustness against display field of view (DFOV) variations are provided. In various embodiments, a system can access a deep learning neural network and a medical image. In various aspects, a first DFOV, and thus a first spatial resolution, on which the deep learning neural network is trained can fail to match a second DFOV, and thus a second spatial resolution, exhibited by the medical image. In various instances, the system can execute the deep learning neural network on a resampled version of the medical image, where the resampled version of the medical image can exhibit the first DFOV and thus the first spatial resolution. In various cases, the system can generate the resampled version of the medical image by up-sampling or down-sampling the medical image until it exhibits the first DFOV and thus the first spatial resolution.

公开(公告)号：US11823354B2

公开(公告)日：2023-11-21

申请号：US17225395

申请日：2021-04-08

Applicant: GE PRECISION HEALTHCARE LLC

Inventor： Bhushan Dayaram Patil ,  Rajesh Langoju ,  Utkarsh Agrawal ,  Bipul Das ,  Jiang Hsieh

IPC: G06T5/00 ,  G16H30/20 ,  G06N3/08 ,  G06T11/00

CPC classification number: G06T5/002 ,  G06N3/08 ,  G06T11/008 ,  G16H30/20 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2211/408

Abstract: A computer-implemented method for correcting artifacts in computed tomography data is provided. The method includes inputting a sinogram into a trained sinogram correction network, wherein the sinogram is missing a pixel value for at least one pixel. The method also includes processing the sinogram via one or more layers of the trained sinogram correction network, wherein processing the sinogram includes deriving complementary information from the sinogram and estimating the pixel value for the at least one pixel based on the complementary information. The method further includes outputting from the trained sinogram correction network a corrected sinogram having the estimated pixel value.

公开(公告)号：US11704804B2

公开(公告)日：2023-07-18

申请号：US16899835

申请日：2020-06-12

Applicant: GE Precision Healthcare LLC

Inventor： Sidharth Abrol ,  Bipul Das ,  Sandeep Dutta ,  Saad A. Sirohey

IPC: G06N5/04 ,  G06T7/11 ,  G06T11/00 ,  G06N20/00 ,  G06T7/10 ,  G06F18/214 ,  G06V10/774

CPC classification number: G06T7/11 ,  G06F18/214 ,  G06N20/00 ,  G06T7/10 ,  G06T11/003 ,  G06V10/7753

Abstract: Techniques are described for domain adaptation of image processing models using post-processing model correction According to an embodiment, a method comprises training, by a system operatively coupled to a processor, a post-processing model to correct an image-based inference output of a source image processing model that results from application of the source image processing model to a target image from a target domain that differs from a source domain, wherein the source image processing model was trained on source images from the source domain. In one or more implementations, the source imaging processing model comprises an organ segmentation model and the post-processing model can comprise a shape-autoencoder. The method further comprises applying, by the system, the source image processing model and the post-processing model to target images from the target domain to generate optimized image-based inference outputs for the target images.

公开(公告)号：US11657501B2

公开(公告)日：2023-05-23

申请号：US17122709

申请日：2020-12-15

Applicant: GE Precision Healthcare LLC

Inventor： Vikram Melapudi ,  Bipul Das ,  Krishna Seetharam Shriram ,  Prasad Sudhakar ,  Rakesh Mullick ,  Sohan Rashmi Ranjan ,  Utkarsh Agarwal

IPC: G06T7/00 ,  G06T5/00 ,  G06T7/10 ,  G06T11/00 ,  A61B6/00

CPC classification number: G06T7/0012 ,  A61B6/482 ,  G06T5/00 ,  G06T7/10 ,  G06T11/003 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/10116 ,  G06T2207/20081

Abstract: Techniques are provided for generating enhanced image representations from original X-ray images using deep learning techniques. In one embodiment, a system is provided that includes a memory storing computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a reception component, an analysis component, and an artificial intelligence component. The analysis component analyzes the original X-ray image using an AI-based model with respect to a set of features of interest. The AI component generates a plurality of enhanced image representations. Each enhanced image representation highlights a subset of the features of interest and suppresses remaining features of interest in the set that are external to the subset.