Patents by Inventor Mohammed S.M. Elbaz

Mohammed S.M. Elbaz 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: 11398303
    Abstract: Described here are systems and methods for generating and analyzing co-expression signature data from scalar or multi-dimensional data fields contained in or otherwise derived from imaging data acquired with a medical imaging system. A similarity metric, such as an angular similarity metric, is computed between the data field components contained in pairs of voxels in the data field data. The data fields can be scalar fields, vector fields, tensor fields, or other higher-dimensional data fields. A probability distribution of these similarity metrics can be generated and used as co-expression signature data that indicate pairwise disparities in the data field data.
    Type: Grant
    Filed: August 27, 2020
    Date of Patent: July 26, 2022
    Assignee: Northwestern University
    Inventors: Mohammed S. M. Elbaz, Michael Markl
  • Publication number: 20220151500
    Abstract: Described here are systems and methods for generating quantitative flow mapping from medical flow data (e.g., medical images, patient-specific computational flow models, particle image velocimetry data, in vitro flow phantom) over a virtual volume representative of a catheter or other medical device. As such, quantitative flow mapping is provided with reduced computational burdens. Quantitative flow maps can also be generated and displayed in a manner that is similar to catheter-based or other medical device-based mapping, without requiring an interventional procedure to place the catheter or medical device.
    Type: Application
    Filed: November 12, 2019
    Publication date: May 19, 2022
    Inventors: Mohammed S.M. Elbaz, Michael Markl
  • Publication number: 20210065875
    Abstract: Described here are systems and methods for generating and analyzing co-expression signature data from scalar or multi-dimensional data fields contained in or otherwise derived from imaging data acquired with a medical imaging system. A similarity metric, such as an angular similarity metric, is computed between the data field components contained in pairs of voxels in the data field data. The data fields can be scalar fields, vector fields, tensor fields, or other higher-dimensional data fields. A probability distribution of these similarity metrics can be generated and used as co-expression signature data that indicate pairwise disparities in the data field data.
    Type: Application
    Filed: August 27, 2020
    Publication date: March 4, 2021
    Inventors: Mohammed S.M. Elbaz, Michael Markl