Patents by Inventor Edward DiBella

Edward DiBella 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).

  • Deep learning reconstruction of free breathing perfusion
    Patent number: 10782378
    Abstract: A method for reducing artifacts in magnetic resonance imaging (MRI) data includes acquiring a k-space dataset of an anatomical subject using a MRI scanner. An iterative compressed sensing reconstruction method is used to generate a reconstructed image based on the k-space dataset. This iterative compressed sensing reconstruction method uses (a) L1-norm based total variation constraints applied the temporal and spatial dimensions of the k-space dataset and (b) a low rank constraint. After the reconstructed image is generated, a deep learning network is used to generate an artifact image depicting motion artifacts present in the reconstructed image. The reconstructed image is subtracted from the artifact image to yield a final image with the motion artifacts removed.
    Type: Grant
    Filed: May 16, 2018
    Date of Patent: September 22, 2020
    Assignees: Siemens Healthcare GmbH, University of Utah Research Foundation
    Inventors: Bradley Drake Bolster, Jr., Ganesh Sharma Adluru Venkata Raja, Edward DiBella
  • Deep Learning Reconstruction of Free Breathing Perfusion
    Publication number: 20190353741
    Abstract: A method for reducing artifacts in magnetic resonance imaging (MRI) data includes acquiring a k-space dataset of an anatomical subject using a MRI scanner. An iterative compressed sensing reconstruction method is used to generate a reconstructed image based on the k-space dataset. This iterative compressed sensing reconstruction method uses (a) L1-norm based total variation constraints applied the temporal and spatial dimensions of the k-space dataset and (b) a low rank constraint. After the reconstructed image is generated, a deep learning network is used to generate an artifact image depicting motion artifacts present in the reconstructed image. The reconstructed image is subtracted from the artifact image to yield a final image with the motion artifacts removed.
    Type: Application
    Filed: May 16, 2018
    Publication date: November 21, 2019
    Inventors: Bradley Drake Bolster, JR., Ganesh Sharma Adluru Venkata Raja, Edward DiBella