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:
Feb. 01, 2022

Filed:

Mar. 15, 2019
Applicant:

Elekta, Inc., Atlanta, GA (US);

Inventor:

Xiao Han, Chesterfield, MO (US);

Assignee:

Elekta, Inc., Atlanta, GA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); G01R 33/56 (2006.01); G06T 5/00 (2006.01); G06T 11/00 (2006.01); G16H 30/40 (2018.01); G16H 30/20 (2018.01); G16H 50/50 (2018.01); A61B 5/055 (2006.01); A61N 5/10 (2006.01); A61B 90/00 (2016.01);
U.S. Cl.
CPC ...
A61B 5/7267 (2013.01); A61B 5/0035 (2013.01); A61B 5/055 (2013.01); A61B 5/7278 (2013.01); A61N 5/1039 (2013.01); G01R 33/5608 (2013.01); G06T 5/007 (2013.01); G06T 11/008 (2013.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/50 (2018.01); A61B 2090/3762 (2016.02); A61B 2576/00 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01);
Abstract

Systems and methods are provided for generating a pseudo-CT prediction model that can be used to generate pseudo-CT images. An exemplary system may include a processor configured to retrieve training data including at least one MR image and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may extract a plurality of features from each image point of the at least one MR image, create a feature vector for each image point based on the extracted features, and extract a CT value from each image point of the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the feature vectors and the CT values of the plurality of training subjects.


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