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公开(公告)号：US20220366535A1
公开(公告)日：2022-11-17
申请号：US17242473
申请日：2021-04-28
Inventor： Yikang Liu , Zhang Chen , Xiao Chen , Shanhui Sun , Terrence Chen
Abstract: An unsupervised machine learning method with self-supervision losses improves a slice-wise spatial resolution of 3D medical images with thick slices, and does not require high resolution images as the ground truth for training. The method utilizes information from high-resolution dimensions to increase a resolution of another desired dimension.
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公开(公告)号：US11488021B2
公开(公告)日：2022-11-01
申请号：US16905115
申请日：2020-06-18
Inventor： Shanhui Sun , Pingjun Chen , Xiao Chen , Zhang Chen , Terrence Chen
Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with image segmentation that may be implementing using an encoder neural network and a decoder neural network. The encoder network may be configured to receive a medical image comprising a visual representation of an anatomical structure and generate a latent representation of the medical image indicating a plurality of features of the medical image. The latent representation may be used by the decoder network to generate a mask for segmenting the anatomical structure from the medical image. The decoder network may be pre-trained to learn a shape prior associated with the anatomical structure and once trained, the decoder network may be used to constrain an output of the encoder network during training of the encoder network.
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公开(公告)号：US11460528B2
公开(公告)日：2022-10-04
申请号：US16936571
申请日：2020-07-23
Inventor： Zhang Chen , Shanhui Sun , Xiao Chen , Terrence Chen
Abstract: An apparatus for magnetic resonance imaging (MRI) image reconstruction is provided. The apparatus accesses a training set of MRI data for training. The training set can include paired fully sampled data or unpaired fully sampled data. Undersampled MRI data is optimized in an MRI data optimization module to generate reconstructed MRI data. The apparatus builds a discriminative model using the training set and the reconstructed MRI data. During inference, the parameters of the discriminator model are fixed and the discriminator model is used to classify an output of the MRI data optimization model as the reconstructed MRI image.
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公开(公告)号：US11423593B2
公开(公告)日：2022-08-23
申请号：US16720602
申请日：2019-12-19
Inventor： Zhang Chen , Shanhui Sun , Terrence Chen
Abstract: Methods and systems for reconstructing an image. For example, a method includes: receiving k-space data; receiving a transform operator corresponding to the k-space data; determining a distribution representing information associated with one or more previous iteration images; generating a next iteration image by an image reconstruction model to reduce an objective function, the objective function corresponding to a data consistency metric and a regularization metric; evaluating whether the next iteration image is satisfactory; and if the next iteration image is satisfactory, outputting the next iteration image as an output image. In certain examples, the data consistency metric corresponds to a first previous iteration image, the k-space data, and the transform operator. In certain examples, the regularization metric corresponds to the distribution. In certain examples, the computer-implemented method is performed by one or more processors.
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公开(公告)号：US11417423B2
公开(公告)日：2022-08-16
申请号：US16986787
申请日：2020-08-06
Inventor： Xiao Chen , Zhang Chen , Shanhui Sun , Terrence Chen
Abstract: A method includes acquiring magnetic resonance imaging (MRI) data with multi-coil dimensions, compressing the coil dimensions to a fixed and predetermined number of virtual coils, and utilizing the fixed and predetermined number of virtual coils by an artificial intelligence engine for artificial intelligence applications.
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Patent Agency Ranking
公开(公告)号：US11308610B2
公开(公告)日：2022-04-19
申请号：US16709982
申请日：2019-12-11
Inventor： Yimo Guo , Shanhui Sun , Terrence Chen
Abstract: A system for generating a bullseye plot of a heart of a subject is provided. The system may obtain multiple slice images in a plurality of groups, wherein each group corresponds to one of a plurality of sections of the heart and includes at least one slice image of the corresponding section, and each slice image includes part of the right ventricle, part of the left ventricle, and part of the myocardium. The system may also identify at least one landmark associated with the left ventricle by applying a landmark detection network in each of the slice images. The system may further generate the bullseye plot of the heart based on the at least one landmark identified in each of the multiple slice images, wherein the bullseye plot includes a plurality of sectors, each of which represents an anatomical region of the myocardium in one of the plurality of sections.
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公开(公告)号：US20210397966A1
公开(公告)日：2021-12-23
申请号：US16905115
申请日：2020-06-18
Inventor： Shanhui Sun , Pingjun Chen , Xiao Chen , Zhang Chen , Terrence Chen
Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with image segmentation that may be implementing using an encoder neural network and a decoder neural network. The encoder network may be configured to receive a medical image comprising a visual representation of an anatomical structure and generate a latent representation of the medical image indicating a plurality of features of the medical image. The latent representation may be used by the decoder network to generate a mask for segmenting the anatomical structure from the medical image. The decoder network may be pre-trained to learn a shape prior associated with the anatomical structure and once trained, the decoder network may be used to constrain an output of the encoder network during training of the encoder network.
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公开(公告)号：US20210192808A1
公开(公告)日：2021-06-24
申请号：US16720602
申请日：2019-12-19
Inventor： Zhang Chen , Shanhui Sun , Terrence Chen
Abstract: Methods and systems for reconstructing an image. For example, a method includes: receiving k-space data; receiving a transform operator corresponding to the k-space data; determining a distribution representing information associated with one or more previous iteration images; generating a next iteration image by an image reconstruction model to reduce an objective function, the objective function corresponding to a data consistency metric and a regularization metric; evaluating whether the next iteration image is satisfactory; and if the next iteration image is satisfactory, outputting the next iteration image as an output image. In certain examples, the data consistency metric corresponds to a first previous iteration image, the k-space data, and the transform operator. In certain examples, the regularization metric corresponds to the distribution. In certain examples, the computer-implemented method is performed by one or more processors.
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公开(公告)号：US12229954B2
公开(公告)日：2025-02-18
申请号：US17953484
申请日：2022-09-27
Inventor： Arun Innanje , Xiao Chen , Shanhui Sun , Zhanhong Wei , Terrence Chen
IPC: G06T7/00
Abstract: An anatomy-aware contouring editing method includes receiving an image, wherein the image represents an anatomically recognizable structure; identifying a first image segment representing part of the anatomically recognizable structure; annotating the first image segment to generate a label of the part; drawing a contour along a boundary of the part; receiving a first input from a user device indicative of a region of contour failure, wherein the region of contour failure includes a portion of a contour that requires editing; editing the contour for generating an edited contour based on the first input and anatomical information; and updating another contour of another part of the anatomically recognizable structure based on the edited contour, wherein the another part is anatomically related to the part.
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公开(公告)号：US12056853B2
公开(公告)日：2024-08-06
申请号：US17565714
申请日：2021-12-30
Inventor： Shanhui Sun , Li Chen , Yikang Liu , Xiao Chen , Zhang Chen
CPC classification number: G06T5/70 , G06T5/10 , G06T5/50 , G06T2207/10121 , G06T2207/20048 , G06T2207/20084 , G06T2207/30052
Abstract: An apparatus for stent visualization includes a hardware processor that is configured to input one or more stent images from a sequence of X-ray images and corresponding balloon marker location data to a cascaded spatial transform network. The background is separated from the one or more stent images using the cascaded spatial transform network and a transformed stent image with a clear background and a non-stent background image is generated. The stent layer and non-stent layer are generated using a neural network without online optimization. A mapping function f maps the inputs, the sequence images and marker coordinates, into the two single image outputs.
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