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:
Aug. 31, 2021

Filed:

Mar. 19, 2020
Applicant:

Elekta Ltd., Montreal, CA;

Inventors:

Martin Emile Lachaine, Montreal, CA;

Silvain Bériault, Longueuil, CA;

Assignee:

Elekta LTD, Montreal, CA;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
A61N 5/10 (2006.01); G06T 7/33 (2017.01); G16H 40/63 (2018.01); G16H 20/40 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 7/20 (2017.01); G06T 7/246 (2017.01); A61B 6/02 (2006.01); A61B 90/00 (2016.01);
U.S. Cl.
CPC ...
A61N 5/1049 (2013.01); A61N 5/1039 (2013.01); A61N 5/1067 (2013.01); A61N 5/1068 (2013.01); G06T 7/20 (2013.01); G06T 7/248 (2017.01); G06T 7/33 (2017.01); G16H 20/40 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 40/63 (2018.01); G16H 50/20 (2018.01); A61B 6/02 (2013.01); A61B 2090/364 (2016.02); A61N 5/1084 (2013.01); A61N 2005/1052 (2013.01); A61N 2005/1054 (2013.01); A61N 2005/1055 (2013.01); A61N 2005/1058 (2013.01); A61N 2005/1061 (2013.01); G06T 2200/04 (2013.01); G06T 2207/10012 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20108 (2013.01); G06T 2207/20216 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Systems and methods are provided for registering images. The systems and methods perform operations comprising: receiving, at a first time point in a given radiation session, a first imaging slice corresponding to a first plane; encoding the first imaging slice to a lower dimensional representation; applying a trained machine learning model to the encoded first imaging slice to estimate an encoded version of a second imaging slice corresponding to a second plane at the first time point to provide a pair of imaging slices for the first time point; simultaneously spatially registering the pair of imaging slices to a volumetric image, received prior to the given radiation session, comprising a time-varying object to calculate displacement of the object; and generating an updated therapy protocol to control delivery of a therapy beam based on the calculated displacement of the object.


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