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. 13, 2024

Filed:

Jul. 07, 2021
Applicant:

Canon Medical Systems Corporation, Otawara, JP;

Inventors:

Yujie Lu, Vernon Hills, IL (US);

Qiulin Tang, Vernon Hills, IL (US);

Zhou Yu, Vernon Hills, IL (US);

Jian Zhou, Vernon Hills, IL (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 5/20 (2006.01); G06N 3/08 (2023.01); G06T 5/50 (2006.01); G16H 30/40 (2018.01);
U.S. Cl.
CPC ...
G06T 5/20 (2013.01); G06N 3/08 (2013.01); G06T 5/50 (2013.01); G16H 30/40 (2018.01); G06T 2200/04 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01);
Abstract

An apparatus, method, and computer-readable medium for improving image quality of a medical volume. In an embodiment, the apparatus includes processing circuitry configured to receive a reconstructed input image volume from X-ray projection data corresponding to a three-dimensional region of an object to be examined, apply a pseudo-three-dimensional neural network (P3DNN) to the reconstructed input image volume, the application of the pseudo-three-dimensional neural network including generating, for the reconstructed input image volume, a plurality of three-dimensional image datasets representing a different anatomical plane of the reconstructed input image volume, applying at least one convolutional filter to each of a sagittal plane dataset, a transverse plane dataset, and a coronal plane dataset, and concatenating results of the applied at least one convolutional filter to generate an intermediate output image volume, and generate, based on the application of the P3DNN, an output image volume corresponding to the three-dimensional region of the object.


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