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.

Date of Patent:
Apr. 25, 2017

Filed:

May. 02, 2013
Applicant:

Esaote Spa, Milan, IT;

Inventors:

Luca Balbi, Genoa, IT;

Amedeo Buonanno, Sant'Arpino, IT;

Paolo Pellegretti, Genoa, IT;

Andrea Serra, Genoa, IT;

Rosario Varriale, Napoli, IT;

Marco Vicari, Freiburg Im Breisgau, DE;

Assignee:

ESAOTE SPA, Genoa, IT;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/055 (2006.01); A61B 8/08 (2006.01); G01R 33/561 (2006.01); G01R 33/56 (2006.01); G01R 33/565 (2006.01);
U.S. Cl.
CPC ...
A61B 8/52 (2013.01); A61B 5/055 (2013.01); G01R 33/561 (2013.01); A61B 8/5207 (2013.01); G01R 33/5608 (2013.01); G01R 33/56509 (2013.01); G01R 33/56563 (2013.01);
Abstract

A method of reconstructing an MRI or ultrasound biomedical image, based on compressed sensing includes exciting a body under examination; acquiring image data from the generated signals, wherein the signals are acquired by pseudo-random undersampling; reconstructing the image using a nonlinear iterative algorithm for minimizing an optimization function containing one or more terms for image data sparsity in one or more predetermined domains with a data fidelity constraint term ensuring fidelity to the acquired image data; wherein two or more sets of image data are acquired from the generated signals, each data set being acquired in a different undersampling scheme and/or a different acquisition mode such as to make expected and unavoidable artifacts incoherent; each of the acquired image data set is multiplied by a correction matrix Δ.


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