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公开(公告)号：US20240090859A1
公开(公告)日：2024-03-21
申请号：US17948822
申请日：2022-09-20
Inventor： Yikang Liu , Zhang Chen , Xiao Chen , Shanhui Sun , Terrence Chen
IPC: A61B6/00
Abstract: A 3D anatomical model of one or more blood vessels of a patient may be obtained using CT angiography, while a 2D image of the blood vessels may be obtained based on fluoroscopy. The 3D model may be registered with the 2D image based on a contrast injection site identified on the 3D model and/or in the 2D image. A fused image may then be created to depict the overlaid 3D model and 2D image, for example, on a monitor or through a virtual reality headset. The injection site may be determined automatically or based on a user input that may include a bounding box drawn around the injection site on the 3D model, a selection of an automatically segmented area in the 3D model, etc.
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公开(公告)号：US11693919B2
公开(公告)日：2023-07-04
申请号：US16908148
申请日：2020-06-22
Inventor： Xiao Chen , Pingjun Chen , Zhang Chen , Terrence Chen , Shanhui Sun
IPC: G06F18/213 , G16H50/50 , G16H30/20 , G16H50/70 , G16H50/20 , G16H30/40 , G06V40/20 , G06N3/08 , G06F18/214
CPC classification number: G06F18/213 , G06F18/2148 , G06N3/08 , G06V40/20 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/50 , G16H50/70 , G06V2201/031
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 utilizing pre-learned knowledge of the anatomy of the anatomical structure. The anatomical knowledge may be learned via a variational autoencoder, which may then be used to optimize the parameters of a motion estimation neural network system such that, when performing motion estimation for the anatomical structure, the motion estimation neural network system may produce results that conform with the underlying anatomy of anatomical structure.
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公开(公告)号：US20230206428A1
公开(公告)日：2023-06-29
申请号：US17564304
申请日：2021-12-29
Inventor： Yikang Liu , Shanhui Sun , Terrence Chen , Zhang Chen , Xiao Chen
CPC classification number: G06T7/0012 , G06N3/02 , G06T7/11 , G06T7/194 , G06T2207/10121 , G06T2207/20081 , G06T2207/20084 , G06T2207/30101
Abstract: Described herein are systems, methods, and instrumentalities associated with image segmentation such as tubular structure segmentation. An artificial neural network is trained to segment tubular structures of interest in a medical scan image based on annotated images of a different type of tubular structures that may have a different contrast and/or appearance from the tubular structures of interest. The training may be conducted in multiple stages during which a segmentation model learned from the annotated images during a first stage may be modified to fit the tubular structures of interest in a second stage. In examples, the tubular structures of interest may include coronary arteries, catheters, guide wires, etc., and the annotated images used for training the artificial neural network may include blood vessels such as retina blood vessels.
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公开(公告)号：US20230187052A1
公开(公告)日：2023-06-15
申请号：US17550594
申请日：2021-12-14
Inventor： Xiao Chen , Shanhui Sun , Terrence Chen
CPC classification number: G16H30/20 , G06T7/0014 , G06T7/60 , A61B5/0044 , A61B5/055 , G06T2207/10088 , G06T2207/30048 , G06T2207/20081 , G06T2207/20084 , A61B2576/023
Abstract: Described herein are systems, methods and instrumentalities associated with automatic assessment of aneurysms. An automatic aneurysm assessment system or apparatus may be configured to obtain, e.g., using a pre-trained artificial neural network, strain values associated one or more locations of a human heart and one or more cardiac phases of the human heart and derive a representation (e.g., a 2D matrix) of the strain values across time and/or space. The system or apparatus may determine, based on the derived representation of the strain values, respective strain patterns associated with the one or more locations of the human heart and further determine whether the one or more locations are aneurysm locations by comparing the automatically determined strain patterns with predetermined normal strain patterns of the heart and determining the presence or risk of aneurysms based on the comparison.
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公开(公告)号：US20230019733A1
公开(公告)日：2023-01-19
申请号：US17378448
申请日：2021-07-16
Inventor： Xiao Chen , Shuo Han , Zhang Chen , Shanhui Sun , Terrence Chen
IPC: G06T5/00 , G06T7/00 , G01R33/48 , G01R33/565
Abstract: Neural network based systems, methods, and instrumentalities may be used to remove motion artifacts from magnetic resonance (MR) images. Such a neural network based system may be trained to perform the motion artifact removal tasks without reference (e.g., without using paired motion-contaminated and motion-free MR images). Various training techniques are described herein including one that feeds the neural network with pairs of MR images with different levels of motion contamination and forces the neural network learn to correct the motion contamination by transforming a first image of a contaminated pair into a second image of the contaminated pair. Other neural network training techniques are also described with an aim to reduce the reliance on training data that is difficult to obtain.
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Patent Agency Ranking
公开(公告)号：US20230014745A1
公开(公告)日：2023-01-19
申请号：US17378465
申请日：2021-07-16
Inventor： Zhang Chen , Shanhui Sun , Xiao Chen , Terrence Chen
Abstract: Disclosed herein are systems, methods, and instrumentalities associated with reconstructing magnetic resonance (MR) images based on under-sampled MR data. The MR data include 2D or 3D information, and may encompass multiple contrasts and multiple coils. The MR images are reconstructed using deep learning (DL) methods, which may accelerate the scan and/or image generation process. Challenges imposed by the large quantity of the MR data and hardware limitations are overcome by separately reconstructing MR images based on respective subsets of contrasts, coils, and/or readout segments, and then combining the reconstructed MR images to obtain desired multi-contrast results.
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公开(公告)号：US20220101537A1
公开(公告)日：2022-03-31
申请号：US17039279
申请日：2020-09-30
Inventor： Shanhui Sun , Hanchao Yu , Xiao Chen , Terrence Chen
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：US20220022817A1
公开(公告)日：2022-01-27
申请号：US16934623
申请日：2020-07-21
Inventor： Zhang Chen , Shanhui Sun , Xiao Chen , Terrence Chen
IPC: A61B5/00 , A61B5/055 , A61B5/0452 , G01R33/48
Abstract: A method includes acquiring MRI data, using an algorithm to predict cardiac cycles from the acquired MRI data, and operating on sections of the acquired MRI data corresponding to selected portions of the predicted cardiac cycles.
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公开(公告)号：US20210157464A1
公开(公告)日：2021-05-27
申请号：US17014609
申请日：2020-09-08
Inventor： Arun Innanje , Xiao Chen , Shanhui Sun , Terrence Chen
Abstract: Cardiac features captured via an MRI scan may be tracked and analyzed using a system described herein. The system may receive a plurality of MR slices derived via the MRI scan and present the MR slices in a manner that allows a user to navigate through the MR slices. Responsive to the user selecting one of the MR slices, contextual and global cardiac information associated with the selected slice may be determined and displayed. The contextual information may correspond to the selected slice and the global information may encompass information gathered across the plurality of MR slices. A user may have the ability to navigate between the different display areas and evaluate the health of the heart with both local and global perspectives.
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公开(公告)号：US12285283B2
公开(公告)日：2025-04-29
申请号：US17948822
申请日：2022-09-20
Inventor： Yikang Liu , Zhang Chen , Xiao Chen , Shanhui Sun , Terrence Chen
Abstract: A 3D anatomical model of one or more blood vessels of a patient may be obtained using CT angiography, while a 2D image of the blood vessels may be obtained based on fluoroscopy. The 3D model may be registered with the 2D image based on a contrast injection site identified on the 3D model and/or in the 2D image. A fused image may then be created to depict the overlaid 3D model and 2D image, for example, on a monitor or through a virtual reality headset. The injection site may be determined automatically or based on a user input that may include a bounding box drawn around the injection site on the 3D model, a selection of an automatically segmented area in the 3D model, etc.
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