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:
Aug. 19, 2014
Filed:
Feb. 07, 2011
Jens Jensen, Scarsdale, NY (US);
Joseph Helpern, Cornwall, NY (US);
Ali Tabesh, New York, NY (US);
Els Fieremans, New York, NY (US);
Jens Jensen, Scarsdale, NY (US);
Joseph Helpern, Cornwall, NY (US);
Ali Tabesh, New York, NY (US);
Els Fieremans, New York, NY (US);
New York University, New York, NY (US);
Abstract
Exemplary method, system, and computer-accessible medium can be provided for determining a measure of diffusional kurtosis by receiving data relating to at least one diffusion weighted image, and determining a measure of a diffusional kurtosis as a function of the received data using a closed form solution procedure. In accordance with certain exemplary embodiments of the present disclosure, provided herein are computer-accessible medium, systems and methods for, e.g., imaging in an MRI system, and, more particularly for facilitating estimation of tensors and tensor-derived measures in diffusional kurtosis imaging (DKI). For example, DKI can facilitate a characterization of non-Gaussian diffusion of water molecules in biological tissues. The diffusion and kurtosis tensors parameterizing the DKI model can typically be estimated via unconstrained least squares (LS) methods. In the presence of noise, motion, and imaging artifacts, these methods can be prone to producing physically and/or biologically implausible tensor estimates. The exemplary embodiments of the present disclosure can address at least this deficiency by formulating an exemplary estimation problem, e.g., as linearly constrained linear LS, where the constraints can ensure acceptable tensor estimates.