Patents by Inventor Ethan M. Johnson

Ethan M. Johnson 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).

  • Publication number: 20260174339
    Abstract: In certain aspects, a method includes analyzing a single 4D flow magnetic resonance imaging (MRI) scan associated with a blood vessel of a patient, wherein the MRI scan is taken at an initial time point. The method includes determining a critical threshold beyond which an area of the blood vessel in the MRI scan fluctuates unboundedly under infinitesimal perturbations. The method includes selecting a base flow comprising a periodic limit cycle following a pulsatile waveform of blood pressure over a cardiac cycle associated with the blood vessel in the MRI scan. The method includes generating a flutter parameter for describing an onset of an instability triggering fluttering of a vessel wall of the blood vessel in the MRI scan, wherein generating the flutter parameter is based on at least the analyzing of the single 4D flow MRI scan, determining the critical threshold, and the selecting the base flow.
    Type: Application
    Filed: November 17, 2023
    Publication date: June 25, 2026
    Inventors: Tom Yu ZHAO, Neelesh A. PATANKAR, Ethan M. JOHNSON, Michael MARKL, Bradley D. ALLEN, Ben Carlton SMITH, GUY ELISHA, Sourav HALDER
  • Patent number: 12579644
    Abstract: Esophageal bolus transport and esophageal mechanics are quantified from medical imaging data, such as dynamic magnetic resonance imaging (“MRI”) data, computed tomography (“CT”) data, or the like. A machine learning model is used to process geometric or other spatiotemporal parameters of a bolus imaged with medical imaging in order to estimate quantitative parameters of the bolus and/or esophagus, such as cross-sectional area, fluid velocity, and fluid pressure. From these values, other parameters can be computed, such as esophageal stiffness and active relaxation.
    Type: Grant
    Filed: August 9, 2023
    Date of Patent: March 17, 2026
    Assignee: Northwestern University
    Inventors: Sourav Halder, Ethan M. Johnson, Jun Yamasaki, Peter J. Kahrilas, Michael Markl, John Erik Pandolfino, Neelesh A. Patankar
  • Publication number: 20240062370
    Abstract: Esophageal bolus transport and esophageal mechanics are quantified from medical imaging data, such as dynamic magnetic resonance imaging (“MRI”) data, computed tomography (“CT”) data, or the like. A machine learning model is used to process geometric or other spatiotemporal parameters of a bolus imaged with medical imaging in order to estimate quantitative parameters of the bolus and/or esophagus, such as cross-sectional area, fluid velocity, and fluid pressure. From these values, other parameters can be computed, such as esophageal stiffness and active relaxation.
    Type: Application
    Filed: August 9, 2023
    Publication date: February 22, 2024
    Inventors: Sourav Halder, Ethan M. Johnson, Jun Yamasaki, Peter J. Kahrilas, Michael Markl, John Erik Pandolfino, Neelesh A. Patankar