Patents by Inventor Alexander Cloninger

Alexander Cloninger 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).

  • Magnetic resonance 2D relaxometry reconstruction using partial data
    Patent number: 10802098
    Abstract: An approach is presented to recontruct image data for an object using a partial set of magnetic resonance (MR) measurements. A subset of data points in a data space representing an object are selected (e.g. through random sampling) for MR data acquisition. Partial MR data corresponding to the subset of data points is received and used for image reconstruction. The overall speed of image reconstruction can be reduced dramatically by relying on acquisition of data for the subset of data points rather than for all data points in the data space representing the object. Compressive sensing type arguments are used to fill in missing measurements, using a priori knowledge of the structure of the data. A compressed data matrix can be recovered from measurements that form a tight frame. It can be established that these measurements satisfy the restricted isometry property (RIP). The zeroth-order regularization minimization problem can then be solved, for example, using a 2D ILT approach.
    Type: Grant
    Filed: April 3, 2020
    Date of Patent: October 13, 2020
    Assignees: The United States of America, as represented by the Secretary, Department of Health and Human Services, University of Maryland, College Park
    Inventors: Peter J. Basser, Ruiliang Bai, Alexander Cloninger, Wojciech Czaja
  • MAGNETIC RESONANCE 2D RELAXOMETRY RECONSTRUCTION USING PARTIAL DATA
    Publication number: 20200233051
    Abstract: An approach is presented to reconstruct image data for an object using a partial set of magnetic resonance (MR) measurements. A subset of data points in a data space representing an object are selected (e.g. through random sampling) for MR data acquisition. Partial MR data corresponding to the subset of data points is received and used for image reconstruction. The overall speed of image reconstruction can be reduced dramatically by relying on acquisition of data for the subset of data points rather than for all data points in the data space representing the object. Compressive sensing type arguments are used to fill in missing measurements, using a priori knowledge of the structure of the data. A compressed data matrix can be recovered from measurements that form a tight frame. It can be established that these measurements satisfy the restricted isometry property (RIP). The zeroth-order regularization minimization problem can then be solved, for example, using a 2D ILT approach.
    Type: Application
    Filed: April 3, 2020
    Publication date: July 23, 2020
    Applicants: The United States of America, as represented by the Secretary, Dept. of Health and Human Services, University of Maryland, College Park
    Inventors: Peter J. Basser, Ruiliang Bai, Alexander Cloninger, Wojciech Czaja
  • Magnetic resonance 2D relaxometry reconstruction using partial data
    Patent number: 10613176
    Abstract: An approach is presented to recontruct image data for an object using a partial set of magnetic resonance (MR) measurements. A subset of data points in a data space representing an object are selected (e.g. through random sampling) for MR data acquisition. Partial MR data corresponding to the subset of data points is received and used for image reconstruction. The overall speed of image reconstruction can be reduced dramatically by relying on acquisition of data for the subset of data points rather than for all data points in the data space representing the object. Compressive sensing type arguments are used to fill in missing measurements, using a priori knowledge of the structure of the data. A compressed data matrix can be recovered from measurements that form a tight frame. It can be established that these measurements satisfy the restricted isometry property (RIP). The zeroth-order regularization minimization problem can then be solved, for example, using a 2D ILT approach.
    Type: Grant
    Filed: April 17, 2015
    Date of Patent: April 7, 2020
    Assignees: The United States of America, as represented by the Secretary, Department of Health and Human Services, University of Maryland, College Park
    Inventors: Peter J. Basser, Ruiliang Bai, Alexander Cloninger, Wojciech Czaja
  • MAGNETIC RESONANCE 2D RELAXOMETRY RECONSTRUCTION USING PARTIAL DATA
    Publication number: 20170089995
    Abstract: An approach is presented to recontruct image data for an object using a partial set of magnetic resonance (MR) measurements. A subset of data points in a data space representing an object are selected (e.g. through random sampling) for MR data acquisition. Partial MR data corresponding to the subset of data points is received and used for image reconstruction. The overall speed of image reconstruction can be reduced dramatically by relying on acquisition of data for the subset of data points rather than for all data points in the data space representing the object. Compressive sensing type arguments are used to fill in missing measurements, using a priori knowledge of the structure of the data. A compressed data matrix can be recovered from measurements that form a tight frame. It can be established that these measurements satisfy the restricted isometry property (RIP). The zeroth-order regularization minimization problem can then be solved, for example, using a 2D ILT approach.
    Type: Application
    Filed: April 17, 2015
    Publication date: March 30, 2017
    Applicants: The United States of America, as represented by the Secretary, Department of Health and Human Serv, University of Maryland, College Park
    Inventors: Peter J. Basser, Ruiliang Bai, Alexander Cloninger, Wojciech Czaja