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. 09, 2024

Filed:

Jun. 04, 2021
Applicant:

Canon Medical Systems Corporation, Otawara, JP;

Inventors:

Jian Zhou, Buffalo Grove, IL (US);

Ruoqiao Zhang, Arlington Heights, IL (US);

Zhou Yu, Wilmette, IL (US);

Yan Liu, Vernon Hills, IL (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 6/00 (2006.01); G06T 11/00 (2006.01); G06T 5/50 (2006.01); G06T 5/20 (2006.01); G06T 5/10 (2006.01); G06N 3/084 (2023.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
A61B 6/5258 (2013.01); A61B 6/4014 (2013.01); G06N 3/045 (2023.01); G06N 3/084 (2013.01); G06T 5/10 (2013.01); G06T 5/20 (2013.01); G06T 5/50 (2013.01); G06T 11/005 (2013.01); G06T 11/006 (2013.01); G06T 11/008 (2013.01); A61B 6/482 (2013.01); A61B 6/5205 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/20064 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2211/408 (2013.01);
Abstract

A deep learning (DL) network corrects/performs sinogram completion in computed tomography (CT) images based on complementary high- and low-kV projection data generated from a sparse (or fast) kilo-voltage (kV)-switching CT scan. The DL network is trained using inputs and targets, which respectively generated with and without kV switching. Another DL network can be trained to correct sinogram-completion errors in the projection data after a basis/material decomposition. A third DL network can be trained to correct sinogram-completion errors in reconstructed images based on the kV-switching projection data. Performance of the DL network can be improved by dividing a 3D convolutional neural network (CNN) into two steps performed by respective 2D CNNs. Further, the projection data and DLL can be divided into high- and low-frequency components to improve performance.


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