-
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.
-
12.发明公开公开(公告)号：US20230419740A1
公开(公告)日：2023-12-28
申请号：US17851190
申请日：2022-06-28
Inventor： Benjamin Planche , Ziyan Wu , Meng Zheng , Terrence Chen
CPC classification number: G06V40/70 , G06T7/0012 , G06V40/15 , G06T2207/10088 , G06T2207/10104 , G06T2207/10108 , G06T2207/10116 , G06T2207/10081
Abstract: A non-invasive biometric system includes a processor that is configured to control a scanner, which is configured to scan and capture one or more anatomical images of a body of a target person. The processor is further configured to identify one or more anatomical structures in the captured one or more anatomical images and extract anatomical features for the identified one or more anatomical structures. The processor is further configured to register the extracted anatomical features for the identified one or more identified anatomical structures to a posture and an external appearance of the target person. The processor is further configured to encode and utilize the extracted anatomical features as biometric data, which is unique for the target person, and may be used for authentication of the target person.
-
公开(公告)号：US11811729B1
公开(公告)日：2023-11-07
申请号：US17889480
申请日：2022-08-17
Inventor： Abhishek Sharma , Arun Innanje , Ziyan Wu , Terrence Chen
IPC: G06F15/16 , H04L61/5046 , H04L61/5038 , H04L61/5014
CPC classification number: H04L61/5046 , H04L61/5014 , H04L61/5038
Abstract: Disclosed is a system and a method for configuring an IP device to be discoverable to a client device over a local network having a DHCP server for assigning dynamic IP addresses. The method includes obtaining a dynamic IP address assigned to the IP device upon completion of boot process for the IP device; checking if a static IP address has been set for the IP device; determining if the dynamic IP address and the static IP address are in a same subnet of the local network; implementing the static IP address set for the IP device, if the dynamic IP address and the static IP address are in the same subnet of the local network; and implementing the dynamic IP address assigned to the IP device, if the dynamic IP address and the static IP address are not in the same subnet of the local network.
-
公开(公告)号：US20230206401A1
公开(公告)日：2023-06-29
申请号：US17564348
申请日：2021-12-29
Inventor： Yikang Liu , Shanhui Sun , Terrence Chen , Zhang Chen , Xiao Chen
CPC classification number: G06T5/002 , G06T5/50 , G06N3/0454 , G06N3/08 , G16H30/40 , G06T2207/20081 , G06T2207/20084 , G06T2207/10064
Abstract: Described herein are systems, methods, and instrumentalities associated with denoising medical images such as fluoroscopic images using deep learning techniques. A first artificial neural network (ANN) is trained to denoise an input medical image in accordance with a provided target noise level. The training of the first ANN is conducted by pairing a noisy input image with target denoised images that include different levels of noise. These target denoised images are generated using a second ANN as intermediate outputs of the second ANN during different training iterations. As such, the first ANN may learn to perform the denoising task in an unsupervised manner without requiring noise-free training images as the ground truth.
-
公开(公告)号：US20230196742A1
公开(公告)日：2023-06-22
申请号：US17557984
申请日：2021-12-21
Inventor： Shanhui Sun , Yikang Liu , Xiao Chen , Zhang Chen , Terrence Chen
IPC: G06V10/774 , G06V10/82 , G06T7/00
CPC classification number: G06V10/7747 , G06V10/82 , G06T7/0012 , 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.
-
Patent Agency Ranking
公开(公告)号：US20230135995A1
公开(公告)日：2023-05-04
申请号：US17513320
申请日：2021-10-28
Inventor： Xiao Chen , Zhang Chen , Shanhui Sun , Terrence Chen
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.
-
17.发明授权公开(公告)号：US11545255B2
公开(公告)日：2023-01-03
申请号：US16722429
申请日：2019-12-20
Inventor： Shanhui Sun , Zhang Chen , Terrence Chen
Abstract: Methods and systems for classifying an image. For example, a method includes: inputting a medical image into a recognition model, the recognition model configured to: generate one or more attribute distributions that are substantially Gaussian when inputted with a normal image; and generate one or more attribute distributions that are substantially non-Gaussian when inputted with an abnormal image; generating, by the recognition model, one or more attribute distributions corresponding to medical image; generating a marginal likelihood corresponding to the likelihood of a sample image substantially matching the medical image, the sample image generated by sampling, by a generative model, the one or more attribute distributions; and generating a classification by at least: if the marginal likelihood is greater than or equal to a predetermined likelihood threshold, determining the image to be normal; and if the marginal likelihood is less than the predetermined likelihood threshold, determining the image to be abnormal.
-
公开(公告)号：US11521323B2
公开(公告)日：2022-12-06
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：US11514573B2
公开(公告)日：2022-11-29
申请号：US17014573
申请日：2020-09-08
Inventor： Qiaoying Huang , Shanhui Sun , Zhang Chen , Terrence Chen
IPC: G06T7/00 , G06T7/11 , G06T7/55 , G06K9/62 , G06N3/04 , G16H50/50 , G16H50/30 , G16H30/40 , G06F3/0485 , G06T11/20 , G06T13/80 , G06T19/00 , G06T7/73 , G06T7/246 , A61B5/00 , A61B5/11 , G06T3/00 , G06N3/08
Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with estimating a thickness of an anatomical structure based on a visual representation of the anatomical structure and a machine-learned thickness prediction model. The visual representation may include an image or a segmentation mask of the anatomical structure. The thickness prediction model may be learned based on ground truth information derived by applying a partial differential equation such as Laplace's equation to the visual representation and solving the partial differential equation. When the visual representation includes an image of the anatomical structure, the systems, methods and instrumentalities described herein may also be capable of generating a segmentation mask of the anatomical structure based on the image.
-
公开(公告)号：US20210272297A1
公开(公告)日：2021-09-02
申请号：US17154450
申请日：2021-01-21
Inventor： Xiao Chen , Shanhui Sun , Terrence Chen
Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with cardiac assessment. An apparatus as described herein may obtain electrocardiographic imaging (ECGI) information associated with a human heart and magnetic resonance imaging (MRI) information associated with the human heart, and integrate the ECGI and MRI information using a machine-learned model. Using the integrated ECGI and MRI information, the apparatus may predict target ablation sites, estimate electrophysiology (EP) measurements, and/or simulate the electrical system of the human heart.
-
-
-
-
-
-
-
-
-
Search Results
125
records
(took 0.025 seconds)
