Patents by Inventor Zhang Chen
Zhang Chen has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 12138015Abstract: 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.Type: GrantFiled: May 5, 2022Date of Patent: November 12, 2024Assignee: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Ziyan Wu, Srikrishna Karanam, Arun Innanje, Shanhui Sun, Abhishek Sharma, Yimo Guo, Zhang Chen
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Patent number: 12141990Abstract: 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.Type: GrantFiled: September 15, 2021Date of Patent: November 12, 2024Assignee: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Shanhui Sun, Zhang Chen, Xiao Chen, Terrence Chen, Junshen Xu
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Patent number: 12141234Abstract: 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.Type: GrantFiled: May 10, 2022Date of Patent: November 12, 2024Assignee: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Xiao Chen, Yikang Liu, Zhang Chen, Shanhui Sun, Terrence Chen, Daniel Hyungseok Pak
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Publication number: 20240373590Abstract: A storage assembly includes a heat dissipation device and a storage. The heat dissipation device includes a thermally conductive block, a heat sink and a plurality of heat pipes. An end of each of the heat pipes is thermally coupled with the thermally conductive block, and another end of each of the heat pipes is thermally coupled with the heat sink. The storage is thermally coupled with the thermally conductive block.Type: ApplicationFiled: June 8, 2023Publication date: November 7, 2024Applicant: COOLER MASTER CO., LTD.Inventors: Bo-Zhang CHEN, Jen-Chih CHENG
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Publication number: 20240331222Abstract: 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.Type: ApplicationFiled: April 3, 2023Publication date: October 3, 2024Applicant: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Shanhui Sun, Zhang Chen, Xiao Chen, Yikang Liu, Terrence Chen
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Publication number: 20240303908Abstract: A method including generating a first vector based on a first grid and a three-dimensional (3D) position associated with a first implicit representation (IR) of a 3D object, generating at least one second vector based on at least one second grid and an upsampled first grid, decoding the first vector to generate a second IR of the 3D object, decoding the at least one second vector to generate at least one third IR of the 3D object, generating a composite IR of the 3D object based on the second IR of the 3D object and the at least one third IR of the 3D object, and generating a reconstructed volume representing the 3D object based on the composite IR of the 3D object.Type: ApplicationFiled: April 30, 2021Publication date: September 12, 2024Inventors: Yinda Zhang, Danhang Tang, Ruofei Du, Zhang Chen, Kyle Genova, Sofien Bouaziz, Thomas Allen Funkhouser, Sean Ryan Francesco Fanello, Christian Haene
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Publication number: 20240303832Abstract: 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.Type: ApplicationFiled: March 9, 2023Publication date: September 12, 2024Applicant: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Xiao Chen, Kun Han, Zhang Chen, Yikang Liu, Shanhui Sun, Terrence Chen
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Publication number: 20240296552Abstract: Disclosed herein are systems, methods, and instrumentalities associated with cardiac motion tracking and/or analysis. In accordance with embodiments of the disclosure, the motion of a heart such as an anatomical component of the heart may be tracked through multiple medical images and a contour of the anatomical component may be outlined in the medical images and presented to a user. The user may adjust the contour in one or more of the medical images and the adjustment may trigger modifications of motion field(s) associated with the one or more medical images, re-tracking of the contour in the one or more medical images, and/or re-determination of a physiological characteristic (e.g., a myocardial strain) of the heart. The adjustment may be made selectively, for example, to a specific medical image or one or more additional medical images selected by the user, without triggering a modification of all of the medical images.Type: ApplicationFiled: March 3, 2023Publication date: September 5, 2024Applicant: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Xiao Chen, Shanhui Sun, Zhang Chen, Yikang Liu, Arun Innanje, Terrence Chen
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Patent number: 12073539Abstract: 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.Type: GrantFiled: December 29, 2021Date of Patent: August 27, 2024Assignee: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Yikang Liu, Shanhui Sun, Terrence Chen, Zhang Chen, Xiao Chen
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Patent number: 12056853Abstract: An apparatus for stent visualization includes a hardware processor that is configured to input one or more stent images from a sequence of X-ray images and corresponding balloon marker location data to a cascaded spatial transform network. The background is separated from the one or more stent images using the cascaded spatial transform network and a transformed stent image with a clear background and a non-stent background image is generated. The stent layer and non-stent layer are generated using a neural network without online optimization. A mapping function f maps the inputs, the sequence images and marker coordinates, into the two single image outputs.Type: GrantFiled: December 30, 2021Date of Patent: August 6, 2024Assignee: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Shanhui Sun, Li Chen, Yikang Liu, Xiao Chen, Zhang Chen
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Patent number: 12045958Abstract: 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.Type: GrantFiled: July 16, 2021Date of Patent: July 23, 2024Assignee: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Xiao Chen, Shuo Han, Zhang Chen, Shanhui Sun, Terrence Chen
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Publication number: 20240233212Abstract: Described herein are systems, methods, and instrumentalities associated with using a multi-layer perceptron (MLP) neural network to process medical images of an anatomical structure. The processing may include padding an input image in accordance with the training of the MLP neural network, splitting the input image (e.g., the padded input image) into patches of a same size, and processing the patches through the MLP neural network over one or more iterations. During an iteration of the processing, the patches may be processed separately and re-combined into an intermediate image before the intermediate image is shifted to concatenate portions of the image that are derived from different patches. This way, global features of the anatomical structure may be learned and used to improve the quality of the image generated by the MLP neural network, without incurring significant computation or memory costs.Type: ApplicationFiled: January 10, 2023Publication date: July 11, 2024Applicant: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Zhang Chen, Shanhui Sun, Xiao Chen, Yikang Liu, Terrence Chen, Chi Zhang
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Patent number: 12013452Abstract: Described herein are systems, methods, and instrumentalities associated with reconstruction of multi-contrast magnetic resonance imaging (MRI) images. The reconstruction may be performed based on under-sampled MRI data collected for the multiple contrasts using corresponding sampling patterns. The sampling patterns and the reconstruction operations for the multiple contrasts may be jointly optimized using deep learning techniques implemented through one or more neural networks. An end-to-end reconstruction optimizing framework is provided with which information collected while processing one contrast may be stored and used for another contrast. A differentiable sampler is described for obtaining the under-sampled MRI data from a k-space and a novel holistic recurrent neural network is used to reconstruct MRI images based on the under-sampled MRI data.Type: GrantFiled: May 10, 2022Date of Patent: June 18, 2024Assignee: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Xiao Chen, Zhang Chen, Yikang Liu, Shanhui Sun, Terrence Chen, Lin Zhao
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Publication number: 20240185417Abstract: Mammography may provide multi-view information about the healthy state of a person's breasts, and described herein are deep learning based techniques for obtaining mammographic images associated with multiple views, extracting cross-view features from the images, and automatically determining the existence or non-existence of a medical abnormality based on the extracted features. The cross-view features may be determined using an encoding module of an artificial neural network (ANN) and the encoded features may be decoded using a decoding module of the ANN to generate a prediction (e.g., a classification label, a bounding shape, a segmentation mask, a probability map, etc.) about the medical abnormality.Type: ApplicationFiled: December 2, 2022Publication date: June 6, 2024Applicant: United Imaging Intelligence (Beijing) Co., Ltd.Inventors: Zhang Chen, Shanhui Sun, Xiao Chen, Yikang Liu, Terrence Chen, Xiangyi Yan
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Patent number: 11992289Abstract: 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.Type: GrantFiled: October 1, 2020Date of Patent: May 28, 2024Assignee: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Zhang Chen, Xiao Chen, Shanhui Sun, Terrence Chen
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Publication number: 20240169486Abstract: 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.Type: ApplicationFiled: November 17, 2022Publication date: May 23, 2024Applicant: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Yikang Liu, Zhang Chen, Xiao Chen, Shanhui Sun, Terrence Chen
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Publication number: 20240153089Abstract: 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.Type: ApplicationFiled: November 7, 2022Publication date: May 9, 2024Applicant: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Xiao Chen, Zhang Chen, Terrence Chen, Shanhui Sun
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Patent number: 11965947Abstract: In Multiplex MRI image reconstruction, a hardware processor acquires sub-sampled Multiplex MRI data and reconstructs parametric images from the sub-sampled Multiplex MRI data. A machine learning model or deep learning model uses the subsampled Multiplex MRI data as the input and parametric maps calculated from the fully sampled data, or reconstructed fully sample data, as the ground truth. The model learns to reconstruct the parametric maps directly from the subsampled Multiplex MRI data.Type: GrantFiled: November 23, 2021Date of Patent: April 23, 2024Assignee: Shanghai United Imaging Intelligence Co., LTD.Inventors: Zhang Chen, Shanhui Sun, Xiao Chen, Terrence Chen
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Patent number: 11966454Abstract: 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.Type: GrantFiled: October 28, 2021Date of Patent: April 23, 2024Assignee: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Zhang Chen, Xiao Chen, Yikang Liu, Terrence Chen, Shanhui Sun
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Patent number: 11967004Abstract: 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.Type: GrantFiled: July 16, 2021Date of Patent: April 23, 2024Assignee: Shanghai United Imaging Intelligence Co., Ltd.Inventors: Zhang Chen, Shanhui Sun, Xiao Chen, Terrence Chen