Patents by Inventor Uten Yarach

Uten Yarach 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).

  • Model-based iterative reconstruction for magnetic resonance imaging with echo planar readout
    Patent number: 12085631
    Abstract: Images are reconstructed from k-space data using a model-based image reconstruction that prospectively and simultaneously accounts for multiple non-idealities in accelerated single-shot-EPI acquisitions. In some implementations, nonlinear regularization (e.g., sparsity regularization) is also incorporated to mitigate noise amplification. The reconstructed images have reduced distortions and noise amplification effects relative to those images that are processed using conventional post-reconstruction techniques to correct for non-idealities.
    Type: Grant
    Filed: April 23, 2020
    Date of Patent: September 10, 2024
    Assignee: May Foundation for Medical Education and Research
    Inventors: Joshua D. Trzasko, Matthew A. Bernstein, Uten Yarach
  • Model-based Nyquist ghost correction for reverse readout echo planar imaging
    Patent number: 12044764
    Abstract: Nyquist ghost artifacts in echo planar imaging (“EPI”) are mitigated, reduced, or otherwise eliminated by implementing robust Nyquist ghost correction (“NGC”) directly from two reversed readout EPI acquisitions. As one advantage, these techniques do not require explicit reference scanning. A model-based process is used for directly estimating statistically optimal NGC coefficients from multi-channel k-space data.
    Type: Grant
    Filed: December 1, 2020
    Date of Patent: July 23, 2024
    Assignee: Mayo Foundation for Medical Education and Research
    Inventors: Joshua D. Trzasko, Uten Yarach, Matthew A. Bernstein, Myung-Ho In, Yi Sui
  • Model-Based Nyquist Ghost Correction for Reverse Readout Echo Planar Imaging
    Publication number: 20220413078
    Abstract: Nyquist ghost artifacts in echo planar imaging (“EPI”) are mitigated, reduced, or otherwise eliminated by implementing robust Nyquist ghost correction (“NGC”) directly from two reversed readout EPI acquisitions. As one advantage, these techniques do not require explicit reference scanning A model-based process is used for directly estimating statistically optimal NGC coefficients from multi-channel k-space data.
    Type: Application
    Filed: December 1, 2020
    Publication date: December 29, 2022
    Inventors: Joshua D. Trzasko, Uten Yarach, Matthew A. Bernstein, Myung-Ho In, Yi Sui
  • Model-Based Iterative Reconstruction for Magnetic Resonance Imaging with Echo Planar Readout
    Publication number: 20220236358
    Abstract: Images are reconstructed from k-space data using a model-based image reconstruction that prospectively and simultaneously accounts for multiple non-idealities in accelerated single-shot-EPI acquisitions. In some implementations, nonlinear regularization (e.g., sparsity regularization) is also incorporated to mitigate noise amplification. The reconstructed images have reduced distortions and noise amplification effects relative to those images that are processed using conventional post-reconstruction techniques to correct for non-idealities.
    Type: Application
    Filed: April 23, 2020
    Publication date: July 28, 2022
    Inventors: Joshua D. Trzasko, Matthew A. Bernstein, Uten Yarach