Patents by Inventor Sona QADIMI

Sona QADIMI 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).

  • Intramyocardial tissue displacement and motion measurement and strain analysis from MRI cine images using DENSE deep learning
    Patent number: 12694518
    Abstract: An exemplary method and system are disclosed that employ DENSE deep learning neural-network(s) trained with displacement-encoded imaging data (i.e., DENSE data) to estimate intramyocardial motion from cine MRI images and other cardiac medical imaging modalities, including standard cardiac computer tomography (CT) images, magnetic resonance imaging (MRI) images, echocardiogram images, heart ultrasound images, among other medical imaging modalities described herein. The DENSE deep learning neural-network(s) can be configured (trained) using (i) contour motion data from displacement-encoded imaging magnitude data as inputs to the neural network and (ii) displacement maps derived from displacement-encoded imaging phase images for comparison to the outputs of the neural network for neural network adjustments during the training.
    Type: Grant
    Filed: February 2, 2022
    Date of Patent: July 28, 2026
    Assignee: UNIVERSITY OF VIRGINIA PATENT FOUNDATION
    Inventors: Frederick H. Epstein, Changyu Sun, Sona Qadimi, Yu Wang
  • INTRAMYOCARDIAL TISSUE DISPLACEMENT AND MOTION MEASUREMENT AND STRAIN ANALYSIS FROM MRI CINE IMAGES USING DENSE DEEP LEARNING
    Publication number: 20240046464
    Abstract: An exemplary method and system are disclosed that employ DENSE deep learning neural-network(s) trained with displacement-encoded imaging data (i.e., DENSE data) to estimate intramyocardial motion from cine MRI images and other cardiac medical imaging modalities, including standard cardiac computer tomography (CT) images, magnetic resonance imaging (MRI) images, echocardiogram images, heart ultrasound images, among other medical imaging modalities described herein. The DENSE deep learning neural-network(s) can be configured (trained) using (i) contour motion data from displacement-encoded imaging magnitude data as inputs to the neural network and (ii) displacement maps derived from displacement-encoded imaging phase images for comparison to the outputs of the neural network for neural network adjustments during the training.
    Type: Application
    Filed: February 2, 2022
    Publication date: February 8, 2024
    Inventors: Frederick H. EPSTEIN, Changyu SUN, Sona QADIMI, Yu WANG