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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
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.
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公开(公告)号：US12141900B2
公开(公告)日：2024-11-12
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
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.
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公开(公告)号：US20230342427A1
公开(公告)日：2023-10-26
申请号：US18343266
申请日：2023-06-28
Applicant: GE Precision Healthcare LLC
Inventor： Tao Tan , Gopal B. Avinash , Máté Fejes , Ravi Soni , Dániel Attila Szabó , Rakesh Mullick , Vikram Melapudi , Krishna Seetharam Shriram , Sohan Rashmi Ranjan , Bipul Das , Utkarsh Agrawal , László Ruskó , Zita Herczeg , Barbara Darázs
IPC: G06F18/214 , G06N5/04 , G16H30/40 , A61B5/055 , G06T5/50 , G06F18/21 , G06T7/30 , A61B5/00 , G16H30/20 , G16H50/20 , G16H50/50 , A61B6/03 , G06F18/22 , G06F18/28 , A61B6/00
CPC classification number: G06F18/214 , A61B5/055 , A61B5/7267 , A61B6/032 , A61B6/5223 , G06F18/2178 , G06F18/22 , G06F18/28 , G06N5/04 , G06T5/50 , G06T7/30 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/50 , G06T2200/04 , G06T2207/10081 , G06T2207/10116 , G06T2207/20081 , G06T2207/20084 , G06T2207/20212 , G06T2207/30004 , G06V2201/03
Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.
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公开(公告)号：US20230052595A1
公开(公告)日：2023-02-16
申请号：US17403017
申请日：2021-08-16
Applicant: GE Precision Healthcare LLC
Inventor： Rajesh Veera Venkata Lakshmi Langoju , Utkarsh Agrawal , Bipul Das , Risa Shigemasa , Yasuhiro Imai , Jiang Hsieh
Abstract: Techniques are described for enhancing the quality of three-dimensional (3D) anatomy scan images using deep learning. 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 a reception component that receives a scan image generated from 3D scan data relative to a first axis of a 3D volume, and an enhancement component that applies an enhancement model to the scan image to generate an enhanced scan image having a higher resolution relative to the scan image. The enhancement model comprises a deep learning neural network model trained on training image pairs respectively comprising a low-resolution scan image and a corresponding high-resolution scan image respectively generated relative to a second axis of the 3D volume.
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公开(公告)号：US20230013779A1
公开(公告)日：2023-01-19
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
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.
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Patent Agency Ranking
公开(公告)号：US20220327664A1
公开(公告)日：2022-10-13
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
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.
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公开(公告)号：US20220101048A1
公开(公告)日：2022-03-31
申请号：US17093960
申请日：2020-11-10
Applicant: GE Precision Healthcare LLC
Inventor： Tao Tan , Gopal B. Avinash , Máté Fejes , Ravi Soni , Dániel Attila Szabó , Rakesh Mullick , Vikram Melapudi , Krishna Seetharam Shriram , Sohan Rashmi Ranjan , Bipul Das , Utkarsh Agrawal , László Ruskó , Zita Herczeg , Barbara Darázs
IPC: G06K9/62 , G06T5/50 , G06T7/30 , G06N5/04 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/50 , A61B6/03 , A61B6/00 , A61B5/055 , A61B5/00
Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.
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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
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.
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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
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.
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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.
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