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:
Oct. 06, 2026

Filed:

Feb. 29, 2024
Applicant:

Siemens Healthineers Ag, Forchheim, DE;

Inventors:

Laura Pfaff, Lohr, DE;

Tobias Würfl, Erlangen, DE;

Marcel Dominik Nickel, Herzogenaurach, DE;

Thomas Benkert, Neunkirchen am Brand, DE;

Assignee:

Siemens Healthineers AG, Forchheim, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 5/60 (2024.01); G06T 5/50 (2006.01); G06T 5/70 (2024.01);
U.S. Cl.
CPC ...
G06T 5/60 (2024.01); G06T 5/50 (2013.01); G06T 5/70 (2024.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20216 (2013.01);
Abstract

A training method for a system with a machine learning model for de-noising images, including: providing numerous image datasets, wherein each image dataset includes a plurality of complex-valued image repetitions; performing a phase correction on the image repetitions, wherein for each provided image repetition of an image dataset a phase-corrected signal image is calculated by amending the phase of the complex-valued image repetition such that the phases of the image repetitions of the image dataset are consistent and such that the signal image comprises signal contribution of the image repetition; calculating a noise map for an image dataset based on the standard deviation between the signal images of this image dataset; and training the machine learning model based on the signal images, the noise map, and a loss function based on Stein's unbiased risk estimator.


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