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公开(公告)号：US20210166445A1
公开(公告)日：2021-06-03
申请号：US16699540
申请日：2019-11-29
发明人： Puyang Wang , Zhang Chen , Shanhui Sun , Terrence Chen
摘要： A system for reconstructing magnetic resonance images includes a processor that is configured to obtain, from a magnetic resonance scanner, sub-sampled k-space data; apply an inverse fast fourier transform to the sub-sampled k-space data to generate a preliminary image; and process the preliminary image via a trained cascaded recurrent neural network to reconstruct a magnetic resonance image.
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公开(公告)号：US20210166427A1
公开(公告)日：2021-06-03
申请号：US16699059
申请日：2019-11-28
发明人： Ziyan WU , Srikrishna KARANAM
摘要： A method for automated calibration is provided. The method may include obtaining a plurality of interest points based on prior information regarding a device and image data of the device captured by a visual sensor. The method may include identifying at least a portion of the plurality of interest points from the image data of the device. The method may also include determining a transformation relationship between a first coordinate system and a second coordinate system based on information of at least a portion of the identified interest points in the first coordinate system and in the second coordinate system that is applied to the visual sensor or the image data of the device.
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93.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
发明人： Zhang Chen , Xiao Chen , Shanhui Sun , Terrence Chen
IPC分类号： G01R33/563 , G06N3/08 , G01R33/36
摘要： 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.
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公开(公告)号：US20210158937A1
公开(公告)日：2021-05-27
申请号：US16860901
申请日：2020-04-28
发明人： Ziyan Wu , Srikrishna Karanam , Arun Innanje , Shanhui Sun , Abhishek Sharma , Yimo Guo , Zhang Chen
摘要： A medical system may utilize a modular and extensible sensing device to derive a two-dimensional (2D) or three-dimensional (3D) human model for a patient in real-time based on images of the patient captured by a sensor such as a digital camera. The 2D or 3D human model may be visually presented on one or more devices of the medical system and used to facilitate a healthcare service provided to the patient. In examples, the 2D or 3D human model may be used to improve the speed, accuracy and consistency of patient positioning for a medical procedure. In examples, the 2D or 3D human model may be used to enable unified analysis of the patient's medical conditions by linking different scan images of the patient through the 2D or 3D human model. In examples, the 2D or 3D human model may be used to facilitate surgical navigation, patient monitoring, process automation, and/or the like.
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公开(公告)号：US20210158550A1
公开(公告)日：2021-05-27
申请号：US16694456
申请日：2019-11-25
发明人： SRIKRISHNA KARANAM , ZIYAN WU
摘要： Methods and systems for using a patient representation model including a feature extraction model and a parameter determining model. For example, a computer-implemented method includes receiving, by a first feature extraction model, a depth image; generating, by the first feature extraction model, a first feature vector corresponding to the depth image; determining, by a parameter determining model, a plurality of three-dimensional model parameters based at least in part on the first feature vector; receiving a ground truth; determining a deviation between the ground truth and information associated with the plurality of three-dimensional model parameters; changing, based at least in part on the deviation, one or more parameters of the patient representation model; receiving a first patient image; determining a plurality of three-dimensional patient parameters based at least in part on the first patient image; and providing the plurality of three-dimensional patient parameters as medical guidance.
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公开(公告)号：US20210158512A1
公开(公告)日：2021-05-27
申请号：US17039355
申请日：2020-09-30
发明人： Shanhui Sun , Hanchao Yu , Xiao Chen , Zhang Chen , Terrence Chen
摘要： 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.
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公开(公告)号：US20210158028A1
公开(公告)日：2021-05-27
申请号：US16995446
申请日：2020-08-17
摘要： The pose and shape of a human body may be recovered based on joint location information associated with the human body. The joint location information may be derived based on an image of the human body or from an output of a human motion capture system. The recovery of the pose and shape of the human body may be performed by a computer-implemented artificial neural network (ANN) trained to perform the recovery task using training datasets that include paired joint location information and human model parameters. The training of the ANN may be conducted in accordance with multiple constraints designed to improve the accuracy of the recovery and by artificially manipulating the training data so that the ANN can learn to recover the pose and shape of the human body even with partially observed joint locations.
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公开(公告)号：US10902651B2
公开(公告)日：2021-01-26
申请号：US16235203
申请日：2018-12-28
发明人： Xiaoqian Huang , Guobin Li , Nan Liu , Yang Xin
摘要： The disclosure relates to systems and methods for magnetic resonance imaging (MRI). A method may include obtaining k-space data associated with MR signals acquired by an MR scanner. The k-space data may corresponding to a first sampling rate. The method may also include generating one or more estimated images based on the k-space data and a target neural network model. The one or more estimated images may correspond to a second sampling rate that exceeds the first sampling rate. The method may further include determining one or more target images based on the one or more estimated images and the k-space data using a compressed sensing model. The compressed sensing model may be constructed based on the one or more estimated images.
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公开(公告)号：US10887558B1
公开(公告)日：2021-01-05
申请号：US16564472
申请日：2019-09-09
发明人： Arun Innanje , Abhishek Sharma , Ziyan Wu , Terrence Chen
摘要： Methods and systems for automatically setting up a sensor connected to an apparatus. For example, a computer-implemented method for automatically setting up a sensor connected to an apparatus includes: receiving a sensor-connection signal corresponding to a connection established between the sensor and the apparatus; determining whether a streaming microservice corresponding to the sensor has been downloaded onto the apparatus; if the streaming microservice has not been downloaded onto the apparatus, determining whether the streaming microservice corresponding to the sensor is supported by the apparatus; if the streaming microservice is supported by the apparatus, downloading a streaming microservice docker from a docker registry, the streaming microservice docker including the streaming microservice and a driver corresponding to the sensor; and deploying the streaming microservice with the driver corresponding to the sensor.
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公开(公告)号：US20200272841A1
公开(公告)日：2020-08-27
申请号：US16870905
申请日：2020-05-09
发明人： Miaofei HAN , Yu ZHANG , Yaozong GAO , Yiqiang ZHAN
IPC分类号： G06K9/20 , G16H50/20 , G16H50/30 , G16H50/50 , G16H30/40 , G06N3/04 , G06K9/62 , G06K9/68 , A61B5/107 , G06T7/11 , G06T7/149 , G06T7/62
摘要： A system for image segmentation is provided. The system may obtain a target image including an ROI, and segment a preliminary region representative of the ROI from the target image using a first ROI segmentation model corresponding to a first image resolution. The system may segment a target region representative of the ROI from the preliminary region using a second ROI segmentation model corresponding to a second image resolution. At least one model of the first and second ROI segmentation models may at least include a first convolutional layer and a second convolutional layer downstream to the first convolutional layer. A count of input channels of the first convolutional layer may be greater than a count of output channels of the first convolutional layer, and a count of input channels of the second convolutional layer may be smaller than a count of output channels of the second convolutional layer.
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