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公开(公告)号：US20250037328A1
公开(公告)日：2025-01-30
申请号：US18359779
申请日：2023-07-26
Applicant: GE Precision Healthcare LLC
Inventor： Elizabeth Janus Nett , Prakhar Prakash , Sandeep Dutta , Michail Fanariotis , Chuck Bisordi
Abstract: Systems and methods are provided for increasing a quality of images generated by a computed tomography (CT) system. In one example, an initial assessment of contrast timing and flow through different anatomical regions of a patient is performed, and based on the initial assessment, different visualization schemes are applied to the different anatomical regions of a reconstructed image, where each visualization is optimized for assessing a different anatomical region. The visualizations may increase a contrast between diseased tissues and non-diseased tissues of the different anatomical regions, and may include color maps (e.g., heat maps and/or probability maps) and/or color overlays based on spectral decomposition information. The different visualizations may be combined into a single 2D image, where each anatomical region (e.g., organ, bone, etc.) is displayed in high contrast, and where aspects of various anatomical regions may be highlighted or colorized to visualize specific information.
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公开(公告)号：US20230071535A1
公开(公告)日：2023-03-09
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
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.
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公开(公告)号：US20230056923A1
公开(公告)日：2023-02-23
申请号：US17407516
申请日：2021-08-20
Applicant: GE Precision Healthcare LLC
Inventor： Jiang Hsieh , Maud Bonnard , Sandeep Dutta
Abstract: Techniques are described for automatically detecting scan characteristics of a medical image series. 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 image generation component that generates a representative image of a medical image series comprising a plurality of scan images, and a series characterization component that processes the representative image using one or more characteristic detection algorithms to determine one or more characteristics of the medical image series. The system can further tailor the visualization layout for viewing the medical image series based on the one or more characteristics and/or automatically perform various workflow tasks based on the one or more characteristics.
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公开(公告)号：US20210097678A1
公开(公告)日：2021-04-01
申请号：US16587923
申请日：2019-09-30
Applicant: GE Precision Healthcare LLC , Partners HealthCare System, Inc. , The General Hospital Corporation , The Brigham and Women's Hospital, Inc.
Inventor： Sandeep Dutta , Ryan Christian King , Bradley Wright , Mitchel Harris , Bharti Khurana , Robert Kevin Moreland
Abstract: Systems and techniques for generating and/or employing a computed tomography (CT) medical imaging fracture model are presented. In one example, a system employs a first convolutional neural network associated with vertebrae segmentation to generate learned vertebrae segmentation data regarding a spine anatomical region related to a CT image. The system also employs a second convolutional neural network associated with fracture segmentation to generate, based on the learned vertebrae segmentation data, learned fracture segmentation data regarding the spine anatomical region. Furthermore, the system detects presence or absence of a medical fracture condition in the CT image based on the learned vertebrae segmentation data and the learned fracture segmentation data.
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公开(公告)号：US20250061605A1
公开(公告)日：2025-02-20
申请号：US18450196
申请日：2023-08-15
Applicant: GE Precision Healthcare LLC
Inventor： Sandeep Dutta , Christopher Philip Bridge , Charles Jiali Lu , Mitchel B Harris , Bharti Khurana , Praveer Singh , Mehak Aggarwal , Sujay Shivanand Kakarmath , Ashwin Vaswani , Amy Deubig , Saad Sirohey , Jayashree Kalpathy-Cramer
Abstract: Systems or techniques that facilitate hybrid 3D-to-2D slice-wise object localization ensembles are provided. In various embodiments, a system can access at least one three-dimensional voxel array. In various aspects, the system can localize, via execution of a deep learning ensemble, an object depicted in the at least one three-dimensional voxel array. In various instances, the deep learning ensemble can receive as input the at least one three-dimensional voxel array. In various cases, the deep learning ensemble can produce as output a set of two-dimensional object location indicators respectively corresponding to a set of two-dimensional slices of the at least one three-dimensional voxel array.
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Patent Agency Ranking
公开(公告)号：US20250037326A1
公开(公告)日：2025-01-30
申请号：US18359760
申请日：2023-07-26
Applicant: GE Precision Healthcare LLC
Inventor： Elizabeth Janus Nett , Prakhar Prakash , Sandeep Dutta , Michail Fanariotis , Chuck Bisordi
Abstract: Systems and methods are provided for increasing a quality of images generated by a computed tomography (CT) system. In one example, an initial assessment of contrast timing and flow through different anatomical regions of a patient is performed, and based on the initial assessment, different visualization schemes are applied to the different anatomical regions of a reconstructed image, where each visualization is optimized for assessing a different anatomical region. In particular, color maps (e.g., heat maps and/or probability maps) and/or color overlays based on material decomposition information may be superimposed on contrast-optimized images, where the color maps accentuate a contrast between diseased tissues and healthy tissues. An automated report may be generated including a first visualization based on a first scan, and a second visualization based on a second, earlier scan, to show a progression of a disease of the patient.
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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.
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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.
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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
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.
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公开(公告)号：US20240282431A1
公开(公告)日：2024-08-22
申请号：US18111159
申请日：2023-02-17
Applicant: GE Precision Healthcare LLC
Inventor： Brian Edward Nett , Bradley Jay Gabrielse , Prakhar Prakash , Sandeep Dutta
CPC classification number: G16H30/20 , G06T7/0014 , G06T7/37 , G06T7/38 , G06T11/008 , G06T2207/30104 , G06T2211/404
Abstract: A computer-implemented method for automatically performing a longitudinal review of medical imaging data via one or more processors includes obtaining, at a computing device, a first image volume acquired of a subject with a medical imaging system of an imaging modality. The method also includes obtaining, at the computing device, a second image volume acquired of the subject with the medical imaging system or another medical imaging system of the imaging modality or a different imaging modality, wherein the first image volume was acquired at an earlier time point than the second image volume. The method further includes automatically aligning, via the computing device, the second image volume to the first image volume to generate aligned image volumes.
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