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. 25, 2020

Filed:

Apr. 27, 2018
Applicant:

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

Inventors:

Jiaofeng Xu, Saint Louis, MO (US);

Xiao Han, Chesterfield, MO (US);

Assignee:

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

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 11/00 (2006.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); G06K 9/66 (2006.01); G06N 3/08 (2006.01); G06T 5/00 (2006.01);
U.S. Cl.
CPC ...
G06T 11/008 (2013.01); A61B 6/032 (2013.01); A61B 6/4085 (2013.01); A61B 6/5282 (2013.01); A61B 6/563 (2013.01); G06K 9/66 (2013.01); G06N 3/08 (2013.01); G06T 5/005 (2013.01); G06T 11/005 (2013.01); G06T 2207/20084 (2013.01); G06T 2210/41 (2013.01); G06T 2211/424 (2013.01);
Abstract

Systems and methods include training a deep convolutional neural network (DCNN) to reduce one or more artifacts using a projection space or an image space approach. In a projection space approach, a method can include collecting at least one artifact contaminated cone beam computed tomography (CBCT) projection space image, and at least one corresponding artifact reduced, CBCT projection space image from each patient in a group of patients, and using the artifact contaminated and artifact reduced CBCT projection space images to train a DCNN to reduce artifacts in a projection space image. In an image space approach, a method can include collecting a plurality of CBCT patient anatomical images and corresponding registered computed tomography anatomical images from a group of patients, and using the plurality of CBCT anatomical images and corresponding artifact reduced computed tomography anatomical images to train a DCNN to remove artifacts from a CBCT anatomical image.


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