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公开(公告)号：US12190508B2
公开(公告)日：2025-01-07
申请号：US17726383
申请日：2022-04-21
Inventor： Yikang Liu , Shanhui Sun , Terrence Chen , Zhang Chen , Xiao Chen
Abstract: Described herein are systems, methods, and instrumentalities associated with medical image enhancement. The medical image may include an object of interest and the techniques disclosed herein may be used to identify the object and enhance a contrast between the object and its surrounding area by adjusting at least the pixels associated with the object. The object identification may be performed using an image filter, a segmentation mask, and/or a deep neural network trained to separate the medical image into multiple layers that respectively include the object of interest and the surrounding area. Once identified, the pixels of the object may be manipulated in various ways to increase the visibility of the object. These may include, for example, adding a constant value to the pixels of the object, applying a sharpening filter to those pixels, increasing the weight of those pixels, and/or smoothing the edge areas surrounding the object of interest.
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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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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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Patent Agency Ranking
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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公开(公告)号：US20230169659A1
公开(公告)日：2023-06-01
申请号：US17538282
申请日：2021-11-30
Inventor： Xiao Chen , Xiaoling Hu , Zhang Chen , Yikang Liu , Terrence Chen , Shanhui Sun
CPC classification number: G06T7/11 , G06T7/149 , G06T3/0093 , G06T3/0006 , G06T7/246 , G06T2207/20084 , G06T2207/20081 , G06T2207/20124 , G06T2207/30048 , G06T2207/10088
Abstract: Described herein are systems, methods, and instrumentalities associated with segmenting and/or determining the shape of an anatomical structure. An artificial neural network (ANN) is used to perform these tasks based on a statistical shape model of the anatomical structure. The ANN is trained by evaluating and backpropagating multiple losses associated with shape estimation and segmentation mask generation. The model obtained using these techniques may be used for different clinical purposes including, for example, motion estimation and motion tracking.
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公开(公告)号：US20230138380A1
公开(公告)日：2023-05-04
申请号：US17513493
申请日：2021-10-28
Inventor： Zhang Chen , Xiao Chen , Yikang Liu , Terrence Chen , Shanhui Sun
IPC: G06K9/62 , G06T7/00 , G06T5/00 , G06T3/40 , G06K9/00 , G06T11/00 , G06N3/08 , G16H30/20 , G01R33/56
Abstract: A neural network system implements a model for generating an output image based on a received input image. The model is learned through a training process during which parameters associated with the model are adjusted so as to maximize a difference between a first image predicted using first parameter values of the model and a second image predicted using second parameter values of the model, and to minimize a difference between the second image and a ground truth image. During a first iteration of the training process the first image is predicted and during a second iteration the second image is predicted. The first parameter values are obtained during the first iteration by minimizing a difference between the first image and the ground truth image, and the second parameter values are obtained during the second iteration by maximizing the difference between the first image and the second image.
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公开(公告)号：US20230079164A1
公开(公告)日：2023-03-16
申请号：US17475534
申请日：2021-09-15
Inventor： Shanhui Sun , Zhang Chen , Xiao Chen , Terrence Chen , Junshen Xu
Abstract: Deep learning based systems, methods, and instrumentalities are described herein for registering images from a same imaging modality and different imaging modalities. Transformation parameters associated with the image registration task are determined using a neural ordinary differential equation (ODE) network that comprises multiple layers, each configured to determine a respective gradient update for the transformation parameters based on a current state of the transformation parameters received by the layer. The gradient updates determined by the multiple ODE layers are then integrated and applied to initial values of the transformation parameters to obtain final parameters for completing the image registration task. The operations of the ODE network may be facilitated by a feature extraction network pre-trained to determine content features shared by the input images. The input images may be resampled into different scales, which are then processed by the ODE network iteratively to improve the efficiency of the ODE operations.
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公开(公告)号：US20220392018A1
公开(公告)日：2022-12-08
申请号：US17340635
申请日：2021-06-07
Inventor： Xiao Chen , Shuo Han , Zhang Chen , Shanhui Sun , Terrence Chen
Abstract: Motion contaminated magnetic resonance (MR) images for training an artificial neural network to remove motion artifacts from the MR images are difficult to obtain. Described herein are systems, methods, and instrumentalities for injecting motion artifacts into clean MR images and using the artificially contaminated images for machine learning and neural network training. The motion contaminated MR images may be created based on clean source MR images that are associated with multiple physiological cycles of a scanned object, and by deriving MR data segments for the multiple physiological cycles based on the source MR images. The MR data segments thus derived may be combined to obtain a simulated MR data set, from which one or more target MR images may be generated to exhibit a motion artifact. The motion artifact may be created by manipulating the source MR images and/or controlling the manner in which the MR data set or the target MR images are generated.
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