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公开(公告)号：US12094080B2
公开(公告)日：2024-09-17
申请号：US17943724
申请日：2022-09-13
Inventor： Yikang Liu , Shanhui Sun , Terrence Chen
IPC: G06T3/4046 , G06T7/11 , G06F3/0482
CPC classification number: G06T3/4046 , G06T7/11 , G06F3/0482 , G06T2200/24 , G06T2207/20081
Abstract: A magnification system for magnifying an image based on trained neural networks is disclosed. The magnification system receives a first user input associated with a selection of a region of interest (ROI) within an input image of a site and a second user input associated with a first magnification factor of the selected ROI. The first magnification factor is associated with a magnification of the ROI in the input image. The ROI is modified based on an application of a first neural network model on the ROI. The modification of the ROI corresponds to a magnified image that is predicted in accordance with the first magnification factor. A display device is controlled to display the modified ROI.
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公开(公告)号：US20240256707A1
公开(公告)日：2024-08-01
申请号：US18103249
申请日：2023-01-30
Inventor： Benjamin Planche , Zikui Cai , Zhongpai Gao , Ziyan Wu , Meng Zheng , Terrence Chen
CPC classification number: G06F21/6254 , G06V10/7715
Abstract: A person's privacy is protected by the law in many settings and disclosed herein are systems, methods, and instrumentalities associated with anonymizing an image of a person while still preserving the visual saliency and/or utility of the image for one or more downstream tasks. These objectives may be accomplished using various machine-learning (ML) techniques such as ML models trained for extracting identifying and residual features from the input image as well as ML models trained for transforming the identifying features into identity-concealing features and for preserving the utility features of the image. An output image may be generated based on the various ML models, wherein the identity of the person may be substantially disguised in the output image while the background and utility attributes of the original image may be substantially maintained in the output image.
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公开(公告)号：US20240153089A1
公开(公告)日：2024-05-09
申请号：US17982023
申请日：2022-11-07
Inventor： Xiao Chen , Zhang Chen , Terrence Chen , Shanhui Sun
CPC classification number: G06T7/0016 , A61B5/0044 , A61B5/055 , A61B5/7267 , G06T7/30 , G06T2207/10088 , G06T2207/20081 , G06T2207/20212 , G06T2207/30048
Abstract: Real-time cardiac MRI images may be captured continuously across multiple cardiac phases and multiple slices. Machine learning-based techniques may be used to determine spatial (e.g., slices and/or views) and temporal (e.g., cardiac cycles and/or cardiac phases) properties of the cardiac images such that the images may be arranged into groups based on the spatial and temporal properties of the images and the requirements of a cardiac analysis task. Different groups of the cardiac MRI images may also be aligned with each other based on the timestamps of the images and/or by synthesizing additional images to fill in gaps.
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公开(公告)号：US11967136B2
公开(公告)日：2024-04-23
申请号：US17557984
申请日：2021-12-21
Inventor： Shanhui Sun , Yikang Liu , Xiao Chen , Zhang Chen , Terrence Chen
IPC: G06V10/774 , G06T7/00 , G06V10/82
CPC classification number: G06V10/7747 , G06T7/0012 , G06V10/82 , G06T2207/30004
Abstract: Described herein are systems, methods, and instrumentalities associated with landmark detection. The detection may be accomplished by determining a graph representation of a plurality of hypothetical landmarks detected in one or more medical images. The graph representation may include nodes that represent the hypothetical landmarks and edges that represent the relationships between paired hypothetical landmarks. The graph representation may be processed using a graph neural network such a message passing graph neural network, by which the landmark detection problem may be converted and solved as a graph node labeling problem.
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公开(公告)号：US11941732B2
公开(公告)日：2024-03-26
申请号：US17513320
申请日：2021-10-28
Inventor： Xiao Chen , Zhang Chen , Shanhui Sun , Terrence Chen
CPC classification number: G06T11/008 , A61B5/055 , A61B5/7267 , G06T7/0012 , G06T2207/10088 , G06T2207/20081 , G06T2207/20084 , G06T2207/30016 , G06T2207/30048
Abstract: Disclosed herein are systems, methods, and instrumentalities associated with reconstructing magnetic resonance (MR) images based on multi-slice, under-sampled MRI data (e.g., k-space data). The multi-slice MRI data may be acquired using a simultaneous multi-slice (SMS) technique and MRI information associated with multiple MRI slices may be entangled in the multi-slice MRI data. A neural network may be trained and used to disentangle the MRI information and reconstruct MRI images for the different slices. A data consistency component may be used to estimate k-space data based on estimates made by the neural network, from which respective MRI images associated with multiple MRI slices may be obtained by applying a Fourier transform to the k-space data.
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Patent Agency Ranking
公开(公告)号：US20240061951A1
公开(公告)日：2024-02-22
申请号：US17891307
申请日：2022-08-19
Inventor： Arun Innanje , Abhishek Sharma , Benjamin Planche , Meng Zheng , Shanhui Sun , Ziyan Wu , Terrence Chen
CPC classification number: G06F21/6245 , H04L9/50 , H04L9/32 , G16H10/60 , G16H50/20 , G06F2221/2141
Abstract: A method and a system for managing healthcare records of a user are provided. The method includes storing an electronic medical record related to the user in form of a non-fungible token (NFT) written to a blockchain, associating a smart contract to the NFT in the blockchain, authorizing a request to access the electronic medical record related to the user based on the defined ownership of the electronic medical record stored in the blockchain, identifying one or more NFTs from the blockchain comprising one or more electronic medical records related to the user based on processing of the identifier information in associated one or more smart contracts therewith, in response to the request, and sending the one or more electronic medical records corresponding to the identified one or more NFTs to a requestor associated with the request.
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公开(公告)号：US20230414132A1
公开(公告)日：2023-12-28
申请号：US17849109
申请日：2022-06-24
Inventor： Abhishek Sharma , Arun Innanje , Benjamin Planche , Meng Zheng , Shanhui Sun , Ziyan Wu , Terrence Chen
CPC classification number: A61B5/1124 , A61B5/1113 , A61B5/749 , A61B5/7267 , A61B5/4848 , G09B19/003 , A61B2505/09 , G06F3/011
Abstract: A system for providing rehabilitation in a virtual environment includes an extended reality (XR) headset to present a first rehabilitation therapy to a patient in a virtual environment. A sensing device is configured to track physical movements of the patient and a processor is configured to receive the sensing data to determine pose information. The processor is configured to determine a performance metric associated with the physical movements and compare the performance metric with a reference metric to determine whether the patient has successfully performed the defined physical movements. The processor is configured to change the first rehabilitation therapy to a second rehabilitation therapy based on a difference between the performance metric and the reference metric upon determining that the patient has unsuccessfully performed the defined physical movements. The system aids the patient by changing the rehabilitation therapies according to the performance of the patient.
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公开(公告)号：US11734837B2
公开(公告)日：2023-08-22
申请号：US17039279
申请日：2020-09-30
Inventor： Shanhui Sun , Hanchao Yu , Xiao Chen , Terrence Chen
CPC classification number: G06T7/248 , G06N3/08 , G06T3/4046 , G06T3/4053 , G06T7/0014 , G06T2207/20016 , G06T2207/20081 , G06T2207/20084 , G06T2207/20221 , G06T2207/30048
Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with estimating the motion of an anatomical structure. The motion estimation may be performed using a feature pyramid and/or a motion pyramid that correspond to multiple image scales. The motion estimation may be performed using neural networks and parameters that are learned via a training process involving a student network and a teacher network pre-pretrained with abilities to apply progressive motion compensation.
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公开(公告)号：US11690579B2
公开(公告)日：2023-07-04
申请号：US16902760
申请日：2020-06-16
Inventor： Srikrishna Karanam , Ziyan Wu , Terrence Chen
IPC: A61B6/00 , A61B6/03 , G06N3/084 , G06F18/22 , G06F18/241 , G06N3/045 , G06V10/774 , G06V10/82 , G06V40/16
CPC classification number: A61B6/032 , A61B6/5211 , A61B6/54 , G06F18/22 , G06F18/241 , G06N3/045 , G06N3/084 , G06V10/774 , G06V10/82 , G06V40/171
Abstract: An apparatus is configured to receive input image data corresponding to output image data of a first radiology scanner device, translate the input image data into a format corresponding to output image data of a second radiology scanner device and generate an output image corresponding to the translated input image data on a post processing imaging device associated with the first radiology scanner device. Medical images from a new scanner can be translate to look as if they came from a scanner of another vendor.
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公开(公告)号：US20230184860A1
公开(公告)日：2023-06-15
申请号：US17550667
申请日：2021-12-14
Inventor： Xiao Chen , Lin Zhao , Zhang Chen , Yikang Liu , Shanhui Sun , Terrence Chen
CPC classification number: G01R33/5601 , G01R33/5608 , G06T7/0012 , G06T2207/20081 , G06T2207/20084
Abstract: Described herein are systems, methods, and instrumentalities associated with generating multi-contrast MRI images associated with an MRI study. The systems, methods, and instrumentalities utilize an artificial neural network (ANN) trained to jointly determine MRI data sampling patterns for the multiple contrasts based on predetermined quality criteria associated with the MRI study and reconstruct MRI images with the multiple contrasts based on under-sampled MRI data acquired using the sampling patterns. The training of the ANN may be conducted with an objective to improve the quality of the whole MRI study rather than individual contrasts. As such, the ANN may learn to allocate resources among the multiple contrasts in a manner that optimizes the performance of the whole MRI study.
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