Patents by Inventor Liyu Shen

Liyu Shen 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: 12249008
    Abstract: A method for diagnostic imaging reconstruction uses a prior image xpr from a scan of a subject to initialize parameters of a neural network which maps coordinates in image space to corresponding intensity values in the prior image. The parameters are initialized by minimizing an objective function representing a difference between intensity values of the prior image and predicted intensity values output from the neural network. The neural network is then trained using subsampled (sparse) measurements of the subject to learn a neural representation of a reconstructed image. The training includes minimizing an objective function representing a difference between the subsampled measurements and a forward model applied to predicted image intensity values output from the neural network. Image intensity values output from the trained neural network from coordinates in image space input to the trained neural network are computed to produce predicted image intensity values.
    Type: Grant
    Filed: September 14, 2022
    Date of Patent: March 11, 2025
    Assignee: The Board of Trustees of the Leland Stanford Junior University
    Inventors: Liyue Shen, Lei Xing
  • Patent number: 12023192
    Abstract: A method for tomographic imaging comprising acquiring [200] a set of one or more 2D projection images [202] and reconstructing [204] a 3D volumetric image [216] from the set of one or more 2D projection images [202] using a residual deep learning network comprising an encoder network, a transform module and a decoder network, wherein the reconstructing comprises: transforming [206] by the encoder network the set of one or more 2D projection images [202] to 2D features [208]; mapping [210] by the transform module the 2D features [208] to 3D features [212]; and generating [214] by the decoder network the 3D volumetric image [216] from the 3D features [212]. Preferably, the encoder network comprises 2D convolution residual blocks and the decoder network comprises 3D blocks without residual shortcuts within each of the 3D blocks.
    Type: Grant
    Filed: November 29, 2019
    Date of Patent: July 2, 2024
    Assignee: The Board of Trustees of the Leland Stanford Junior University
    Inventors: Liyue Shen, Wei Zhao, Lei Xing
  • Publication number: 20230368438
    Abstract: A method for medical imaging performs a sparse-sampled tomographic imaging acquisition by an imaging system to produce acquired sparse imaging samples; synthesizes by a first deep learning network unacquired imaging samples from the acquired imaging samples to produce complete imaging samples comprising both the acquired imaging samples and unacquired imaging samples; transforms by a physics module the complete imaging samples to image space data based on physics and geometry priors of the imaging system; and performs image refinement by a second deep learning network to produce tomographic images from the image space data. The physics and geometry priors of the imaging system comprise geometric priors of a physical imaging model of the imaging system, and prior geometric relationships between the sample and image data domains.
    Type: Application
    Filed: May 12, 2023
    Publication date: November 16, 2023
    Inventors: Liyue Shen, Lei Xing, Lianli Liu
  • Publication number: 20230024401
    Abstract: A method for diagnostic imaging reconstruction uses a prior image xpr from a scan of a subject to initialize parameters of a neural network which maps coordinates in image space to corresponding intensity values in the prior image. The parameters are initialized by minimizing an objective function representing a difference between intensity values of the prior image and predicted intensity values output from the neural network. The neural network is then trained using subsampled (sparse) measurements of the subject to learn a neural representation of a reconstructed image. The training includes minimizing an objective function representing a difference between the subsampled measurements and a forward model applied to predicted image intensity values output from the neural network. Image intensity values output from the trained neural network from coordinates in image space input to the trained neural network are computed to produce predicted image intensity values.
    Type: Application
    Filed: September 14, 2022
    Publication date: January 26, 2023
    Inventors: Liyue Shen, Lei Xing
  • Publication number: 20220414953
    Abstract: Image reconstruction is an inverse problem that solves for a computational image based on sampled sensor measurement. Sparsely sampled image reconstruction poses addition challenges due to limited measurements. In this work, we propose an implicit Neural Representation learning methodology with Prior embedding (NeRP) to reconstruct a computational image from sparsely sampled measurements. The method differs fundamentally from previous deep learning-based image reconstruction approaches in that NeRP exploits the internal information in an image prior, and the physics of the sparsely sampled measurements to produce a representation of the unknown subject. No large-scale data is required to train the NeRP except for a prior image and sparsely sampled measurements. In addition, we demonstrate that NeRP is a general methodology that generalizes to different imaging modalities such as CT and MRI.
    Type: Application
    Filed: June 8, 2022
    Publication date: December 29, 2022
    Inventors: Liyue Shen, Lei Xing
  • Publication number: 20210393229
    Abstract: A method for tomographic imaging comprising acquiring [200] a set of one or more 2D projection images [202] and reconstructing [204] a 3D volumetric image [216] from the set of one or more 2D projection images [202] using a residual deep learning network comprising an encoder network, a transform module and a decoder network, wherein the reconstructing comprises: transforming [206] by the encoder network the set of one or more 2D projection images [202] to 2D features [208]; mapping [210] by the transform module the 2D features [208] to 3D features [212]; and generating [214] by the decoder network the 3D volumetric image from the 3D features [212]. Preferably, the encoder network comprises 2D convolution residual blocks and the decoder network comprises 3D blocks without residual shortcuts within each of the 3D blocks.
    Type: Application
    Filed: November 29, 2019
    Publication date: December 23, 2021
    Inventors: Liyue Shen, Wei Zhao, Lei Xing
  • Publication number: 20080022348
    Abstract: The present invention provides an interactive video display system and a method thereof for displaying video clips with a plurality of marks thereon. The present invention utilizes the sound, movements of a moving object, various movement directions or acting signals generated from a computer to produce a plurality of acting signals for controlling the display of the video clips incorporated with the sound effect to increase the reality of the image display. Marks are provided on the vide clip to indicates the movements or the exercising frequencies or the directions of the movements. The present invention provides a method and a system that can directly interact the user with the visual images in the video screen.
    Type: Application
    Filed: November 7, 2006
    Publication date: January 24, 2008
    Inventor: Liyu Shen