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. 18, 2022

Filed:

Jul. 12, 2019
Applicant:

Canon Medical Systems Corporation, Otawara, JP;

Inventors:

Tzu-Cheng Lee, Vernon Hills, IL (US);

Jian Zhou, Vernon Hills, IL (US);

Zhou Yu, Vernon Hills, IL (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); A61B 6/00 (2006.01); G06N 3/08 (2006.01); A61B 6/03 (2006.01); G06N 3/04 (2006.01); G06N 20/00 (2019.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01);
U.S. Cl.
CPC ...
A61B 6/584 (2013.01); G06N 3/084 (2013.01); G06T 7/0012 (2013.01); A61B 6/032 (2013.01); G06N 3/04 (2013.01); G06N 20/00 (2019.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01);
Abstract

A method and apparatus is provided that uses a deep learning (DL) network to correct projection images acquired using an X-ray source with a large focal spot size. The DL network is trained using a training dataset that includes input data and target data. The input data includes large-focal-spot-size X-ray projection data, and the output data includes small-focal-spot-size X-ray projection data (i.e., smaller than the focal spot of the input data). Thus, the DL network is trained to improve the resolution of projection data acquired using a large focal spot size, and obtain a resolution similar to what is achieved using a small focal spot size. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., denoising the projection data).


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