Patents by Inventor Cristian DONOS

Cristian DONOS 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).

  • Methods for optimizing the planning and placement of probes in the brain via multimodal 3D analyses of cerebral anatomy
    Patent number: 11734842
    Abstract: A method includes obtaining a first imaging scan and a second imaging scan of a single subject brain. The first imaging scan is converted to a first dataset, and the second imaging scan is converted to a second dataset. A sequence-adaptive multimodal segmentation algorithm is applied to the first dataset and the second dataset. The sequence-adaptive multimodal segmentation algorithm performs automatic intensity-based tissue classification to generate a first labelled dataset and a second labeled dataset. The first labeled dataset and the second labeled dataset are automatically co-registered to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset. The transformation matrix is applied to align the first dataset and the second dataset.
    Type: Grant
    Filed: February 22, 2021
    Date of Patent: August 22, 2023
    Assignee: THE BOARD OF REGENTS OF THE UNIVERSITY OF TEXAS SYSTEM
    Inventors: Nitin Tandon, Mehmet Kadipasaoglu, Kevin Pham, Cristian Donos, Kiefer Forseth, Patrick Sarahan Rollo
  • METHODS FOR OPTIMIZING THE PLANNING AND PLACEMENT OF PROBES IN THE BRAIN VIA MULTIMODAL 3D ANALYSES OF CEREBRAL ANATOMY
    Publication number: 20210264623
    Abstract: A method includes obtaining a first imaging scan and a second imaging scan of a single subject brain. The first imaging scan is converted to a first dataset, and the second imaging scan is converted to a second dataset. A sequence-adaptive multimodal segmentation algorithm is applied to the first dataset and the second dataset. The sequence-adaptive multimodal segmentation algorithm performs automatic intensity-based tissue classification to generate a first labelled dataset and a second labeled dataset. The first labeled dataset and the second labeled dataset are automatically co-registered to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset. The transformation matrix is applied to align the first dataset and the second dataset.
    Type: Application
    Filed: February 22, 2021
    Publication date: August 26, 2021
    Inventors: Nitin TANDON, Mehmet KADIPASAOGLU, Kevin PHAM, Cristian DONOS, Kiefer FORSETH