Patents by Inventor Jeeva P. Munasinghe

Jeeva P. Munasinghe 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).

  • Denoising of dynamic resonance spectroscopic imaging using low rank approximations in the kinetic domain
    Patent number: 12270881
    Abstract: Kinetic monitoring of in vivo metabolism of labelled tracers is based on singular value decomposition or Tucker Decomposition of magnetic resonance spectral image data. Data decomposition is used in conjunction with rank reduction to improve signal-to-noise ratio. Rank reduction can be applied in one or more of a spectral, spatial, or temporal dimension. Rank is generally reduced based on a number of expected analytes/metabolites or fit of measured data to a model.
    Type: Grant
    Filed: January 14, 2022
    Date of Patent: April 8, 2025
    Assignee: The United States of America, as represented by the Secretary, Department of Health and Human
    Inventors: Jeffrey R. Brender, James B. Mitchell, Kazutoshi Yamamoto, Shun Kishimoto, Jeeva P. Munasinghe, Hellmut Merkle, Murali K. Cherukuri
  • DENOISING OF DYNAMIC RESONANCE SPECTROSCOPIC IMAGING USING LOW RANK APPROXIMATIONS IN THE KINETIC DOMAIN
    Publication number: 20220214415
    Abstract: Kinetic monitoring of in vivo metabolism of labelled tracers is based on singular value decomposition or Tucker Decomposition of magnetic resonance spectral image data. Data decomposition is used in conjunction with rank reduction to improve signal-to-noise ratio. Rank reduction can be applied in one or more of a spectral, spatial, or temporal dimension. Rank is generally reduced based on a number of expected analytes/metabolites or fit of measured data to a model.
    Type: Application
    Filed: January 14, 2022
    Publication date: July 7, 2022
    Inventors: Jeffrey R. Brender, James B. Mitchell, Kazutoshi Yamamoto, Shun Kishimoto, Jeeva P. Munasinghe, Hellmut Merkle, Murali K. Cherukuri
  • DENOISING OF DYNAMIC MAGNETIC RESONANCE SPECTROSCOPIC IMAGING USING LOW RANK APPROXIMATIONS IN THE KINETIC DOMAIN
    Publication number: 20200049782
    Abstract: Kinetic monitoring of in vivo metabolism of labelled tracers is based on singular value decomposition or Tucker Decomposition of magnetic resonance spectral image data. Data decomposition is used in conjunction with rank reduction to improve signal-to-noise ratio. Rank reduction can be applied in one or more of a spectral, spatial, or temporal dimension. Rank is generally reduced based on a number of expected analytes/metabolites or fit of measured data to a model.
    Type: Application
    Filed: February 14, 2018
    Publication date: February 13, 2020
    Applicant: The United States of America, as represented by the Secretary, Department of Health and Human Ser..
    Inventors: Jeffrey R. Brender, James B. Mitchell, Kazutoshi Yamamoto, Shun Kishimoto, Jeeva P. Munasinghe, Hellmut Merkle, Murali K. Cherukuri