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:
Jan. 02, 2024

Filed:

Feb. 05, 2021
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Stephan Kannengiesser, Wuppertal, DE;

Berthold Kiefer, Erlangen, DE;

Tommaso Mansi, Plainsboro, NJ (US);

Marcel Dominik Nickel, Herzogenaurach, DE;

Thomas Pheiffer, Philadelphia, PA (US);

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06T 7/00 (2017.01); G16H 10/60 (2018.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01); A61B 5/055 (2006.01); A61B 5/00 (2006.01); G01R 33/48 (2006.01); G01R 33/56 (2006.01); G06N 3/08 (2023.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 5/055 (2013.01); A61B 5/4244 (2013.01); A61B 5/7267 (2013.01); G01R 33/4828 (2013.01); G01R 33/5608 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G16H 10/60 (2018.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01); A61B 2576/02 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30056 (2013.01);
Abstract

The disclosure relates to techniques for automatically characterizing liver tissue of a patient, comprising receiving morphological magnetic resonance image data set and at least one magnetic resonance parameter map of an imaging region comprising at least partially the liver of the patient, each acquired by a magnetic resonance imaging device, via a first interface. The techniques further include applying a trained function comprising a neural network to input data comprising at least the image data set and the parameter map. At least one tissue score describing the liver tissue is generated as output data, which is provided using a second interface.


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