The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Oct. 13, 2020
Filed:
Apr. 03, 2020
The United States of America, As Represented BY the Secretary, Department of Health and Human Services, Bethesda, MD (US);
University of Maryland, College Park, College Park, MD (US);
Peter J. Basser, Washington, DC (US);
Ruiliang Bai, Bethesda, MD (US);
Alexander Cloninger, New Haven, CT (US);
Wojciech Czaja, Silver Spring, MD (US);
The United States of America, as represented by the Secretary, Department of Health and Human Services, Bethesda, MD (US);
University of Maryland, College Park, College Park, MD (US);
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.