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:
Nov. 20, 2018

Filed:

Mar. 28, 2017
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Yi Guo, Los Angeles, CA (US);

Boris Mailhe, Plainsboro, NJ (US);

Xiao Chen, Somerset, NJ (US);

Mariappan S. Nadar, Plainsboro, NJ (US);

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/66 (2006.01); G06T 11/00 (2006.01); G06T 7/00 (2017.01); A61B 5/055 (2006.01); A61B 5/00 (2006.01);
U.S. Cl.
CPC ...
G06K 9/66 (2013.01); A61B 5/055 (2013.01); A61B 5/7267 (2013.01); G06T 7/0012 (2013.01); G06T 11/008 (2013.01); G06T 2207/10088 (2013.01);
Abstract

A method for training a system for reconstructing a magnetic resonance image includes: under-sampling image data from each of a plurality of fully-sampled images; and inputting the under-sampled image data to a multi-scale neural network comprising sequentially connected layers. Each layer has an input for receiving input image data and an output for outputting reconstructed image data. Each layer performs a process comprising: decomposing the array of input image data; applying a thresholding function to the decomposed image data, to form a shrunk data, the thresholding function outputting a value asymptotically approaching one when the thresholding function receives an input having a magnitude greater than a first value, reconstructing the shrunk data for combining with a reconstructed image data output by another one of the layers to form updated reconstructed image data, and machine-learning at least one parameter of the decomposing, the thresholding function, or the reconstructing.


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