公开(公告)号：US12272023B2

公开(公告)日：2025-04-08

申请号：US17654864

申请日：2022-03-15

Applicant: GE Precision Healthcare LLC

Inventor： Bipul Das ,  Rakesh Mullick ,  Deepa Anand ,  Sandeep Dutta ,  Uday Damodar Patil ,  Maud Bonnard

IPC: G06T3/60 ,  G06T7/73 ,  G06V10/774 ,  G06V10/82 ,  G16H50/20

Abstract: Systems/techniques that facilitate deep learning multi-planar reformatting of medical images are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can localize, via execution of a machine learning model, a set of landmarks depicted in the three-dimensional medical image, a set of principal anatomical planes depicted in the three-dimensional medical image, and a set of organs depicted in the three-dimensional medical image. In various instances, the system can determine an anatomical orientation exhibited by the three-dimensional medical image, based on the set of landmarks, the set of principal anatomical planes, or the set of organs. In various cases, the system can rotate the three-dimensional medical image, such that the anatomical orientation now matches a predetermined anatomical orientation.

公开(公告)号：US20250095143A1

公开(公告)日：2025-03-20

申请号：US18471188

申请日：2023-09-20

Applicant: GE Precision Healthcare LLC

Inventor： Rajesh Langoju ,  Utkarsh Agrawal ,  Bipul Das ,  Risa Shigemasa ,  Yasuhiro Imai ,  Kok Yen Tham ,  Yuri Teraoka

IPC: G06T7/00 ,  G06T7/11

Abstract: Methods and systems are provided for transforming images from one energy level to another. In an example, a method includes obtaining an image at a first energy level, identifying a contrast phase of the image, entering the image as input to a segmentation model trained to output an anatomy mask that identifies each tissue type in the image, generating a guide image from the image and the anatomy mask using a regression model, entering the image and the guide image as input into an energy transformation model trained to output a transformed image at a different, second energy level, the energy transformation model selected from among a plurality of energy transformation models based on the contrast phase, and displaying a final transformed image and/or saving the final transformed image in memory, wherein the final transformed image is the transformed image or is generated based on the transformed image.

公开(公告)号：US12217417B2

公开(公告)日：2025-02-04

申请号：US17470076

申请日：2021-09-09

Applicant: GE Precision Healthcare LLC ,  University of Zurich

Inventor： Sidharth Abrol ,  Bipul Das ,  Vanika Singhal ,  Amy Deubig ,  Sandeep Dutta ,  Daphné Gerbaud ,  Bianca Sintini ,  Ronny Büchel ,  Philipp Kaufmann

IPC: G06T7/00 ,  G06N20/00 ,  G06T5/60 ,  G06T5/70 ,  G06T7/10 ,  G06T7/11 ,  G06V10/25 ,  G16H30/40

Abstract: Systems/techniques that facilitate learning-based domain transformation for medical images are provided. In various embodiments, a system can access a medical image. In various aspects, the medical image can depict an anatomical structure according to a first medical scanning domain. In various instances, the system can generate, via execution of a machine learning model, a predicted image based on the medical image. In various aspects, the predicted image can depict the anatomical structure according to a second medical scanning domain that is different from the first medical scanning domain. In some cases, the first and second medical scanning domains can be first and second energy levels of a computed tomography (CT) scanning modality. In other cases, the first and second medical scanning domains can be first and second contrast phases of the CT scanning modality.

公开(公告)号：US12156752B2

公开(公告)日：2024-12-03

申请号：US17444881

申请日：2021-08-11

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：US20230177747A1

公开(公告)日：2023-06-08

申请号：US17543234

申请日：2021-12-06

Applicant: GE Precision Healthcare LLC

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

IPC: G06T11/00 ,  G06T5/00 ,  G06T5/50 ,  G06N20/20

CPC classification number: G06T11/008 ,  G06N20/20 ,  G06T5/002 ,  G06T5/50

Abstract: Systems/techniques that facilitate machine learning generation of low-noise and high structural conspicuity images are provided. In various embodiments, a system can access an image and can apply at least one of image denoising or image resolution enhancement to the image, thereby yielding a first intermediary image. In various instances, the system can generate, via execution of a plurality of machine learning models, a plurality of second intermediary images based on the first intermediary image, wherein a given machine learning model in the plurality of machine learning models receives as input the first intermediary image, wherein the given machine learning model produces as output a given second intermediary image in the plurality of second intermediary images, and wherein the given second intermediary image represents a kernel-transformed version of the first intermediary image. In various cases, the system can generate a blended image based on the plurality of second intermediary images.

公开(公告)号：US20230084202A1

公开(公告)日：2023-03-16

申请号：US17474711

申请日：2021-09-14

Applicant: GE Precision Healthcare LLC

Inventor： Abhijit Patil ,  Rakesh Mullick ,  Bipul Das

IPC: G06F21/62

Abstract: Techniques are described that that facilitate securely deploying artificial intelligence (AI) models and distributing inferences generated therefrom. 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 an algorithm execution component that applies an AI model to input data and generates output data, and an encryption component that encrypts the output data using a proprietary encryption mechanism, resulting in encrypted output data. The proprietary encryption mechanism can include a mechanism that prevents usage and rendering of the encrypted output data without decryption of the encrypted output data using a proprietary decryption mechanism.

公开(公告)号：US20220092768A1

公开(公告)日：2022-03-24

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

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.

公开(公告)号：US20250095239A1

公开(公告)日：2025-03-20

申请号：US18471181

申请日：2023-09-20

Applicant: GE Precision Healthcare LLC

Inventor： Rajesh Langoju ,  Utkarsh Agrawal ,  Bipul Das ,  Risa Shigemasa ,  Yasuhiro Imai ,  Kok Yen Tham ,  Yuri Teraoka

IPC: G06T11/00 ,  G06V10/764 ,  G06V10/774 ,  G06V20/50 ,  G16H30/40

Abstract: Various methods and systems are provided for transforming images from one energy level to another. In an example, a method includes obtaining an image at a first energy level acquired with a single-energy computed tomography (CT) imaging system, identifying a contrast phase of the image, entering the image as input into an energy transformation model trained to output a transformed image at a second energy level, different than the first energy level, the energy transformation model selected from among a plurality of energy transformation models based on the contrast phase, and displaying a final transformed image and/or saving the final transformed image in memory, wherein the final transformed image is the transformed image or is generated based on the transformed image.

公开(公告)号：US12249023B2

公开(公告)日：2025-03-11

申请号：US18065964

申请日：2022-12-14

Applicant: GE Precision Healthcare LLC

Inventor： Deepa Anand ,  Bipul Das ,  Vanika Singhal ,  Rakesh Mullick ,  Sanjay Kumar NT

IPC: G06T15/20 ,  G06T15/08 ,  G06V10/82

Abstract: Systems/techniques that facilitate interpretable task-specific dimensionality-reduction are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can generate, via execution of a first deep learning neural network, a voxel-wise weight map corresponding to the three-dimensional medical image and a set of projection vectors corresponding to the three-dimensional medical image. In various instances, the system can generate a set of two-dimensional projection images of the three-dimensional medical image, based on the voxel-wise weight map and the set of projection vectors. In various cases, the first deep learning neural network can be trained in a serial pipeline with a second deep learning neural network that is configured to perform an inferencing task on two-dimensional inputs. This can cause the set of two-dimensional projection images to be tailored to the inferencing task.

公开(公告)号：US20250045951A1

公开(公告)日：2025-02-06

申请号：US18362224

申请日：2023-07-31

Applicant: GE Precision Healthcare LLC

Inventor： Bipul Das ,  Deepa Anand ,  Vanika Singhal ,  Rakesh Mullick

IPC: G06T7/73 ,  G06T7/00 ,  G06V10/774 ,  G06V10/82 ,  G06V20/50 ,  G06V20/70 ,  G16H30/40

Abstract: Systems/techniques that facilitate explainable confidence estimation for landmark localization are provided. In various embodiments, a system can access a three-dimensional voxel array captured by a medical imaging scanner and can localize, via execution of a first deep learning neural network, a set of anatomical landmarks depicted in the three-dimensional voxel array. In various aspects, the system can generate a multi-tiered confidence score collection based on the set of anatomical landmarks and based on a training dataset on which the first deep learning neural network was trained. In various instances, the system can, in response to one or more confidence scores from the multi-tiered confidence score collection failing to satisfy a threshold, generate, via execution of a second deep learning neural network, a classification label that indicates an explanatory factor for why the one or more confidence scores failed to satisfy the threshold.