-
公开(公告)号：US20220122259A1
公开(公告)日：2022-04-21
申请号：US17076641
申请日：2020-10-21
Inventor： Yimo Guo , Xiao Chen , Shanhui Sun , Terrence Chen
Abstract: A bullseyes plot may be generated based on cardiac magnetic resonance imaging (CMRI) to facilitate the diagnosis and treatment of heart diseases. Described herein are systems, methods, and instrumentalities associated with bullseyes plot generation. A plurality of myocardial segments may be obtained for constructing the bullseye plot based on landmark points detected in short-axis and long-axis magnetic resonance (MR) slices of the heart and by arranging the short-axis MR slices sequentially in accordance with the order in which the slices are generated during the CMRI. The sequential order of the short-axis MR slices may be determined utilizing projected locations of the short-axis MR slices on a long-axis MR slice and respective distances of the projected locations to a landmark point of the long-axis MR slice. The myocardium and/or landmark points may be identified in the short-axis and/or long-axis MR slices using artificial neural networks.
-
公开(公告)号：US20210397886A1
公开(公告)日：2021-12-23
申请号：US16908148
申请日：2020-06-22
Inventor： Xiao Chen , Pingjun Chen , Zhang Chen , Terrence Chen , Shanhui Sun
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.
-
13.发明申请公开(公告)号：US20210165064A1
公开(公告)日：2021-06-03
申请号：US17060988
申请日：2020-10-01
Inventor： Zhang Chen , Xiao Chen , Shanhui Sun , Terrence Chen
IPC: G01R33/563 , G06N3/08 , G01R33/36
Abstract: A method includes using fully sampled retro cine data to train an algorithm, and applying the trained algorithm to real time MR cine data to yield reconstructed MR images.
-
公开(公告)号：US20210158512A1
公开(公告)日：2021-05-27
申请号：US17039355
申请日：2020-09-30
Inventor： Shanhui Sun , Hanchao Yu , Xiao Chen , Zhang Chen , Terrence Chen
Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with imagery data processing. The neural networks may be pre-trained to learn parameters or models for processing the imagery data and upon deployment the neural networks may automatically perform further optimization of the learned parameters or models based on a small set of online data samples. The online optimization may be facilitated via offline meta-learning so that the optimization may be accomplished quickly in a few optimization steps.
-
公开(公告)号：US12045958B2
公开(公告)日：2024-07-23
申请号：US17378448
申请日：2021-07-16
Inventor： Xiao Chen , Shuo Han , Zhang Chen , Shanhui Sun , Terrence Chen
IPC: G06T5/70 , G01R33/48 , G01R33/565 , G06T7/00
CPC classification number: G06T5/70 , G01R33/4818 , G01R33/56509 , G06T7/0014 , G06T2207/10088 , G06T2207/20081 , G06T2207/20084
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.
-
Patent Agency Ranking
公开(公告)号：US20240169486A1
公开(公告)日：2024-05-23
申请号：US17989205
申请日：2022-11-17
Inventor： Yikang Liu , Zhang Chen , Xiao Chen , Shanhui Sun , Terrence Chen
CPC classification number: G06T5/50 , G06T5/002 , G06T5/003 , G06T2207/10016 , G06T2207/10121 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004
Abstract: Deblurring and denoising a medical image such as X-ray fluoroscopy images may be challenging, and deep-learning based techniques may be employed to meet the challenge. An artificial neural network (ANN) may be trained using training images with synthetic noise and as well as training images with real noise. The parameters of the ANN may be adjusted during the training based on at least a first loss designed to maintain continuity between consecutive medical images generated by the ANN and a second loss designed to maintain similarity of patches inside a medical image generated by the ANN. The parameters of the ANN may be further adjusted based on a third loss that may be calculated from ground truth associated with the synthetic training images. Transfer learning between the synthetic images and the real images may be accomplished using these techniques.
-
公开(公告)号：US11967004B2
公开(公告)日：2024-04-23
申请号：US17378465
申请日：2021-07-16
Inventor： Zhang Chen , Shanhui Sun , Xiao Chen , Terrence Chen
IPC: G06T11/00 , A61B5/00 , A61B5/055 , G06F18/214 , G06F18/22 , G06K9/62 , G06N3/04 , G06N3/08 , G06T5/50
CPC classification number: G06T11/005 , A61B5/055 , A61B5/7267 , G06F18/214 , G06F18/22 , G06N3/04 , G06N3/08 , G06T5/50 , G06T2207/10088 , G06T2207/20081 , G06T2207/20084 , G06T2207/20221
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.
-
公开(公告)号：US11948288B2
公开(公告)日：2024-04-02
申请号：US17340635
申请日：2021-06-07
Inventor： Xiao Chen , Shuo Han , Zhang Chen , Shanhui Sun , Terrence Chen
CPC classification number: G06T5/70 , G06N3/08 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/70 , G06T2207/10088 , G06T2207/20056 , G06T2207/20081 , G06T2207/20084 , G16H50/50
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.
-
公开(公告)号：US20240062047A1
公开(公告)日：2024-02-22
申请号：US17891702
申请日：2022-08-19
Inventor： Zhang Chen , Siyuan Dong , Shanhui Sun , Xiao Chen , Yikang Liu , Terrence Chen
IPC: G06N3/04
CPC classification number: G06N3/0472 , G06N3/0454 , G06T2207/20081 , G06T2207/20084
Abstract: Deep learning-based systems, methods, and instrumentalities are described herein for MRI reconstruction and/or refinement. An MRI image may be reconstructed based on under-sampled MRI information and a generative model may be trained to refine the reconstructed image, for example, by increasing the sharpness of the MRI image without introducing artifacts into the image. The generative model may be implemented using various types of artificial neural networks including a generative adversarial network. The model may be trained based on an adversarial loss and a pixel-wise image loss, and once trained, the model may be used to improve the quality of a wide range of 2D or 3D MRI images including those of a knee, brain, or heart.
-
公开(公告)号：US20240013510A1
公开(公告)日：2024-01-11
申请号：US17858663
申请日：2022-07-06
Inventor： Yikang Liu , Luojie Huang , Zhang Chen , Xiao Chen , Shanhui Sun
IPC: G06V10/75 , G06V10/764 , G06T7/73 , G06N3/04 , G06N3/08
CPC classification number: G06V10/751 , G06V10/764 , G06T7/73 , G06N3/0454 , G06N3/08 , G06T2207/20084 , G06T2207/20081
Abstract: Described herein are systems, methods, and instrumentalities associated with tracking groups of small objects in medical images. The tracking may be accomplished by, for each one of a sequence of medical images, determining a plurality of candidate objects captured in the medical image, grouping the plurality of candidate objects into a plurality of groups of candidate objects and dividing the medical image into a plurality of regions that each surrounds a corresponding group of candidate objects. Each of the plurality of regions may be examined to extract respective features associated with each corresponding group of candidate objects. A match between a first group of candidate objects in a first medical image and a second group of candidate objects in a second medical image may be determined based on first features associated with the first group and second features associated with the second group.
-
-
-
-
-
-
-
-
-
Search Results
75
records
(took 0.023 seconds)
