Patents by Inventor JOSEPH YITAN CHENG

JOSEPH YITAN CHENG 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).

  • Patent number: 11125846
    Abstract: A method is disclosed for phase contrast magnetic resonance imaging (MRI) comprising: acquiring phase contrast 3D spatiotemporal MRI image data; inputing the 3D spatiotemporal MRI image data to a three-dimensional spatiotemporal convolutional neural network to produce a phase unwrapping estimate; generating from the phase unwrapping estimate an integer number of wraps per pixel; and combining the integer number of wraps per pixel with the phase contrast 3D spatiotemporal MRI image data to produce final output.
    Type: Grant
    Filed: March 20, 2020
    Date of Patent: September 21, 2021
    Assignee: The Board of Trustees of the Leland Stanford Junior University
    Inventors: Christopher Michael Sandino, Shreyas S. Vasanawala, Joseph Yitan Cheng, Jiacheng Jason He
  • Publication number: 20210286429
    Abstract: Techniques are disclosed for defining a training data set to include biosignals and categorical labels representative of a context. For example, a categorical label may indicate whether a user was performing a difficult or easy mental task while the biosignal was being recorded. A set of first layers in a neural network can be trained using a portion of the training data set associated with a first set of users and at least one second layer can be trained using a portion of the training data set associated with a particular other user. The neural network can then be used to process other biosignals from the particular other user to generate predicted categorical context labels.
    Type: Application
    Filed: February 12, 2021
    Publication date: September 16, 2021
    Applicant: Apple Inc.
    Inventors: Erdrin Azemi, Joseph Yitan Cheng, Hanlin Goh
  • Patent number: 11085988
    Abstract: A method for magnetic resonance imaging (MRI) includes steps of acquiring by an MRI scanner undersampled magnetic-field-gradient-encoded k-space data; performing a self-calibration of a magnetic-field-gradient-encoding point-spread function using a first neural network to estimate systematic waveform errors from the k-space data, and computing the magnetic-field-gradient-encoding point-spread function from the systematic waveform errors; reconstructing an image using a second neural network from the magnetic-field-gradient-encoding point-spread function and the k-space data.
    Type: Grant
    Filed: March 19, 2020
    Date of Patent: August 10, 2021
    Assignee: The Board of Trustees of the Leland Stanford Junior University
    Inventors: Feiyu Chen, Christopher Michael Sandino, Joseph Yitan Cheng, John M. Pauly, Shreyas S. Vasanawala
  • Patent number: 11062490
    Abstract: A magnetic resonance imaging scan performs an MRI acquisition using an undersampling pattern to produce undersampled k-space data; adds the undersampled k-space data to aggregate undersampled k-space data for the scan; reconstructs an image from the aggregate undersampled k-space data; updates the undersampling pattern from the reconstructed image and aggregate undersampled k-space data using a deep reinforcement learning technique defined by an environment, reward, and agent, where the environment comprises an MRI reconstruction technique, where the reward comprises an image quality metric, and where the agent comprises a deep convolutional neural network and fully connected layers; and repeats these steps to produce a final reconstructed MRI image for the scan.
    Type: Grant
    Filed: October 16, 2019
    Date of Patent: July 13, 2021
    Assignee: The Board of Trustees of the Leland Stanford Junior University
    Inventors: David Y. Zeng, Shreyas S. Vasanawala, Joseph Yitan Cheng
  • Publication number: 20200300955
    Abstract: A method is disclosed for phase contrast magnetic resonance imaging (MRI) comprising: acquiring phase contrast 3D spatiotemporal MRI image data; inputing the 3D spatiotemporal MRI image data to a three-dimensional spatiotemporal convolutional neural network to produce a phase unwrapping estimate; generating from the phase unwrapping estimate an integer number of wraps per pixel; and combining the integer number of wraps per pixel with the phase contrast 3D spatiotemporal MRI image data to produce final output.
    Type: Application
    Filed: March 20, 2020
    Publication date: September 24, 2020
    Inventors: Christopher Michael Sandino, Shreyas S. Vasanawala, Joseph Yitan Cheng, Jiacheng Jason He
  • Publication number: 20200300957
    Abstract: A method for magnetic resonance imaging (MRI) includes steps of acquiring by an MRI scanner undersampled magnetic-field-gradient-encoded k-space data; performing a self-calibration of a magnetic-field-gradient-encoding point-spread function using a first neural network to estimate systematic waveform errors from the k-space data, and computing the magnetic-field-gradient-encoding point-spread function from the systematic waveform errors; reconstructing an image using a second neural network from the magnetic-field-gradient-encoding point-spread function and the k-space data.
    Type: Application
    Filed: March 19, 2020
    Publication date: September 24, 2020
    Inventors: Feiyu Chen, Christopher Michael Sandino, Joseph Yitan Cheng, John M. Pauly, Shreyas S. Vasanawala
  • Publication number: 20200249300
    Abstract: Various methods and systems are provided for reconstructing magnetic resonance images from accelerated magnetic resonance imaging (MM) data. In one embodiment, a method for reconstructing a magnetic resonance (MR) image includes: estimating multiple sets of coil sensitivity maps from undersampled k-space data, the undersampled k-space data acquired by a multi-coil radio frequency (RF) receiver array; reconstructing multiple initial images using the undersampled k-space data and the estimated multiple sets of coil sensitivity maps; iteratively reconstructing, with a trained deep neural network, multiple images by using the initial images and the multiple sets of coil sensitivity maps to generate multiple final images, each of the multiple images corresponding to a different set of the multiple sets of sensitivity maps; and combining the multiple final images output from the trained deep neural network to generate the MR image.
    Type: Application
    Filed: February 5, 2019
    Publication date: August 6, 2020
    Inventors: Christopher Michael Sandino, Peng Lai, Shreyas Vasanawala, Joseph Yitan Cheng
  • Patent number: 10712416
    Abstract: Various methods and systems are provided for reconstructing magnetic resonance images from accelerated magnetic resonance imaging (MRI) data. In one embodiment, a method for reconstructing a magnetic resonance (MR) image includes: estimating multiple sets of coil sensitivity maps from undersampled k-space data, the undersampled k-space data acquired by a multi-coil radio frequency (RF) receiver array; reconstructing multiple initial images using the undersampled k-space data and the estimated multiple sets of coil sensitivity maps; iteratively reconstructing, with a trained deep neural network, multiple images by using the initial images and the multiple sets of coil sensitivity maps to generate multiple final images, each of the multiple images corresponding to a different set of the multiple sets of sensitivity maps; and combining the multiple final images output from the trained deep neural network to generate the MR image.
    Type: Grant
    Filed: February 5, 2019
    Date of Patent: July 14, 2020
    Assignees: GE PRECISION HEALTHCARE, LLC, THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY
    Inventors: Christopher Michael Sandino, Peng Lai, Shreyas Vasanawala, Joseph Yitan Cheng
  • Patent number: 10692250
    Abstract: A method for magnetic resonance imaging acquires multi-channel subsampled k-space data using multiple receiver coils; performs singular-value-decomposition on the multi-channel subsampled k-space data to produce compressed multi-channel k-space data which normalizes the multi-channel subsampled k-space data; applies a first center block of the compressed multi-channel k-space data as input to a first convolutional neural network to produce a first estimated k-space center block that includes estimates of k-space data missing from the first center block; generates an n-th estimated k-space block by repeatedly applying an (n?1)-th estimated k-space center block combined with an n-th center block of the compressed multi-channel k-space data as input to an n-th convolutional neural network to produce an n-th estimated k-space center block that includes estimates of k-space data missing from the n-th center block; reconstructs image-space data from the n-th estimated k-space block.
    Type: Grant
    Filed: January 29, 2019
    Date of Patent: June 23, 2020
    Assignee: The Board of Trustees of the Leland Stanford Junior University
    Inventors: Joseph Yitan Cheng, Morteza Mardani Korani, John M. Pauly, Shreyas S. Vasanawala
  • Publication number: 20200134887
    Abstract: A magnetic resonance imaging scan performs an MRI acquisition using an undersampling pattern to produce undersampled k-space data; adds the undersampled k-space data to aggregate undersampled k-space data for the scan; reconstructs an image from the aggregate undersampled k-space data; updates the undersampling pattern from the reconstructed image and aggregate undersampled k-space data using a deep reinforcement learning technique defined by an environment, reward, and agent, where the environment comprises an MRI reconstruction technique, where the reward comprises an image quality metric, and where the agent comprises a deep convolutional neural network and fully connected layers; and repeats these steps to produce a final reconstructed MRI image for the scan.
    Type: Application
    Filed: October 16, 2019
    Publication date: April 30, 2020
    Inventors: David Y. Zeng, Shreyas S. Vasanawala, Joseph Yitan Cheng
  • Publication number: 20190236817
    Abstract: A method for magnetic resonance imaging acquires multi-channel subsampled k-space data using multiple receiver coils; performs singular-value-decomposition on the multi-channel subsampled k-space data to produce compressed multi-channel k-space data which normalizes the multi-channel subsampled k-space data; applies a first center block of the compressed multi-channel k-space data as input to a first convolutional neural network to produce a first estimated k-space center block that includes estimates of k-space data missing from the first center block; generates an n-th estimated k-space block by repeatedly applying an (n?1)-th estimated k-space center block combined with an n-th center block of the compressed multi-channel k-space data as input to an n-th convolutional neural network to produce an n-th estimated k-space center block that includes estimates of k-space data missing from the n-th center block; reconstructs image-space data from the n-th estimated k-space block.
    Type: Application
    Filed: January 29, 2019
    Publication date: August 1, 2019
    Inventors: Joseph Yitan Cheng, Morteza Mardani Korani, John M. Pauly, Shreyas S. Vasanawala
  • Patent number: 10185016
    Abstract: A method for phase-contrast imaging a fluid within a volume of an imaged subject is provided. The method includes acquiring a plurality of slabs, each slab imaging the fluid flowing within a portion of the volume; and volume merging the plurality of slabs to form an image of the volume. Each slab of the plurality is aligned with respect to the volume such that each slab of the plurality is continuously supplied with a plurality of magnetically unsaturated portions of the fluid during acquisition.
    Type: Grant
    Filed: April 22, 2016
    Date of Patent: January 22, 2019
    Assignees: General Electric Company, The Board of Trustees of the Leland Stanford Junior University
    Inventors: Peng Lai, Joseph Yitan Cheng
  • Publication number: 20170307713
    Abstract: A method for phase-contrast imaging a fluid within a volume of an imaged subject is provided. The method includes acquiring a plurality of slabs, each slab imaging the fluid flowing within a portion of the volume; and volume merging the plurality of slabs to form an image of the volume. Each slab of the plurality is aligned with respect to the volume such that each slab of the plurality is continuously supplied with a plurality of magnetically unsaturated portions of the fluid during acquisition.
    Type: Application
    Filed: April 22, 2016
    Publication date: October 26, 2017
    Applicants: GENERAL ELECTRIC COMPANY, THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY
    Inventors: PENG LAI, JOSEPH YITAN CHENG