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公开(公告)号：US20230367850A1
公开(公告)日：2023-11-16
申请号：US17741323
申请日：2022-05-10
Inventor： Xiao Chen , Yikang Liu , Zhang Chen , Shanhui Sun , Terrence Chen , Daniel Hyungseok Pak
CPC classification number: G06K9/6256 , A61B5/7267 , G01R33/5608 , G06N20/00 , G06T11/005
Abstract: Described herein are systems, methods, and instrumentalities associated with processing complex-valued MRI data using a machine learning (ML) model. The ML model may be learned based on synthetically generated MRI training data and by applying one or more meta-learning techniques. The MRI training data may be generated by adding phase information to real-valued MRI data and/or by converting single-coil MRI data into multi-coil MRI data based on coil sensitivity maps. The meta-learning process may include using portions of the training data to conduct a first round of learning to determine updated model parameters and using remaining portions of the training data to test the updated model parameters. Losses associated with the testing may then be determined and used to refine the model parameters. The ML model learned using these techniques may be adopted for a variety of tasks including, for example, MRI image reconstruction and/or de-noising.
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公开(公告)号：US11803939B2
公开(公告)日：2023-10-31
申请号：US17242473
申请日：2021-04-28
Inventor： Yikang Liu , Zhang Chen , Xiao Chen , Shanhui Sun , Terrence Chen
CPC classification number: G06T3/4053 , G06N3/088 , G06T3/4046 , G06T11/006 , G16H30/40
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：US20220338816A1
公开(公告)日：2022-10-27
申请号：US17236173
申请日：2021-04-21
Inventor： Xiao Chen , Abhishek Sharma , Terrence Chen , Shanhui Sun
Abstract: A system and method for cardiac function and myocardial strain analysis include techniques and structure for classifying a set of cardiac images according to their views, detecting a heart range and valid short-axis slices in the set of cardiac images, determining heart segment locations, segmenting heart anatomies for each time frame and each slice, calculating volume related parameters, determining key physiological time points, calculating myocardium transmural thickness and deriving a cardiac function measure from the myocardium transmural thickness at the key physiological time points, estimating a dense motion field from the key physiological time points as applied to the set of cardiac images, calculating myocardial strain along different myocardium directions from the dense motion field, and providing the cardiac function measure and myocardial strain calculation to a user through a user interface.
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公开(公告)号：US11315246B2
公开(公告)日：2022-04-26
申请号：US17014609
申请日：2020-09-08
Inventor： Arun Innanje , Xiao Chen , Shanhui Sun , Terrence Chen
IPC: G06T7/12 , G06T7/00 , G06T7/11 , G06K9/62 , G06N3/04 , G16H50/50 , G16H50/30 , G16H30/40 , G06F3/0485 , G06T11/20 , G06T13/80 , G06T19/00 , G06T7/55 , G06T7/73 , G06T7/246 , A61B5/00 , A61B5/11 , G06T3/00 , G06N3/08
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：US20220026514A1
公开(公告)日：2022-01-27
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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Patent Agency Ranking
公开(公告)号：US20210161422A1
公开(公告)日：2021-06-03
申请号：US17060860
申请日：2020-10-01
Inventor： Xiao Chen , Shanhui Sun , Zhang Chen , Terrence Chen
IPC: A61B5/055 , A61B5/00 , G06N3/08 , G01R33/561
Abstract: A method includes acquiring initial scout images of a patient's heart, using a neural network to establish a patient specific heart model, and automatically plan imaging planes of the patient specific heart model, performing an accelerated scan of the patient's heart, using the neural network to determine a current location and pose of the patient's heart from the accelerated scan, and to reposition the imaging planes to correspond to the current location and pose of the patient's heart, and using the repositioned imaging planes to perform an acquisition scan and generate an image of the patient's heart from the acquisition scan according to a selected imaging protocol.
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公开(公告)号：US20250025054A1
公开(公告)日：2025-01-23
申请号：US18223414
申请日：2023-07-18
Inventor： Yikang Liu , Dehong Fang , Lin Zhao , Zhang Chen , Xiao Chen , Shanhui Sun , Terrence Chen
Abstract: Described herein are systems, methods, and instrumentalities associated with automatic determination of hemodynamic characteristics. An apparatus as described may implement a first artificial neural network (ANN) and a second ANN. The first ANN may model a mapping from a set of 3D points associated with one or more blood vessels to a set of hemodynamic characteristics of the one or more blood vessels, while the second ANN may generate, based on a geometric relationship of the set of points in a 3D space, parameters for controlling the mapping. The apparatus may obtain a 3D anatomical model representing at least one blood vessel of a patient based on one or more medical images of the patient, and determine, based on the first ANN and the second ANN, a hemodynamic characteristic of the at least one blood vessel of the patient at a target location of the 3D anatomical model.
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公开(公告)号：US12178641B2
公开(公告)日：2024-12-31
申请号：US17814223
申请日：2022-07-21
Inventor： Shanhui Sun , Ziyan Wu , Xiao Chen , Zhang Chen , Yikang Liu , Arun Innanje , Terrence Chen
Abstract: The present disclosure provides a system and method for fetus monitoring. The method may include obtaining ultrasound data relating to a fetus collected by an ultrasound imaging device; generating a 4D image of the fetus based on the ultrasound data; directing a display component of a virtual reality (VR) device to display the 4D image to an operator; detecting motion of the fetus based on the ultrasound data; and directing a haptic component of the VR device to provide haptic feedback with respect to the motion to the operator.
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公开(公告)号：US20240331222A1
公开(公告)日：2024-10-03
申请号：US18130150
申请日：2023-04-03
Inventor： Shanhui Sun , Zhang Chen , Xiao Chen , Yikang Liu , Terrence Chen
IPC: G06T11/00
CPC classification number: G06T11/005 , G06T2210/41 , G06T2211/424
Abstract: Disclosed herein are systems, methods, and instrumentalities associated with magnetic resonance (MR) image reconstruction. An under-sampled MR image may be reconstructed through an iterative process (e.g., over multiple iterations) based on a machine-learning (ML) model. The ML model may be obtained through a reinforcement learning process during which the ML model may be used to predict a correction to an input MR image of at least one of the multiple iterations, apply the correction to the input MR image to obtain a reconstructed MR image, determine a reward for the ML model based on the reconstructed MR image, and adjust the parameters of the ML model based on the reward. The reward may be determined using a pre-trained reward neural network and the ML model may also be pre-trained in a supervised manner before being refined through the reinforcement learning process.
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公开(公告)号：US20240303832A1
公开(公告)日：2024-09-12
申请号：US18119435
申请日：2023-03-09
Inventor： Xiao Chen , Kun Han , Zhang Chen , Yikang Liu , Shanhui Sun , Terrence Chen
CPC classification number: G06T7/251 , G06T7/215 , G06T2207/20081 , G06T2207/20084 , G06T2207/30048
Abstract: The motion estimation of an anatomical structure may be performed using a machine-learned (ML) model trained based on medical training images of the anatomical structure and corresponding segmentation masks for the anatomical structure. During the training of the ML model, the model may be used to predict a motion field that may indicate a change between a first training image and a second training image, and to transform the first training image and a corresponding first segmentation mask based on the motion field. The parameters of the ML model may then be adjusted to maintain a correspondence between the transformed first training image and the second training image and between the transformed first segmentation mask or a second segmentation mask associated with the second training image. The correspondence may be assessed based on at least a boundary region shared by the anatomical structure and one or more other anatomical structures.
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