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:
Oct. 01, 2024

Filed:

Aug. 01, 2023
Applicant:

Zhejiang Lab, Zhejiang, CN;

Inventors:

Jingsong Li, Hangzhou, CN;

Yiwei Gao, Hangzhou, CN;

Peijun Hu, Hangzhou, CN;

Tianshu Zhou, Hangzhou, CN;

Yu Tian, Hangzhou, CN;

Assignee:

ZHEJIANG LAB, Hangzhou, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 11/00 (2006.01); G06T 3/4053 (2024.01); G06T 5/70 (2024.01); G16H 30/40 (2018.01);
U.S. Cl.
CPC ...
G06T 11/006 (2013.01); G06T 3/4053 (2013.01); G06T 5/70 (2024.01); G06T 11/005 (2013.01); G16H 30/40 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30168 (2013.01); G06T 2211/441 (2023.08);
Abstract

The present application discloses a label-free adaptive CT super-resolution reconstruction method, device and system based on a generative network, which comprises the following modules: an acquisition module configured for acquiring low-resolution original CT image data; a preprocessing module configured for performing super-resolution reconstruction on original CT images based on total variation to obtain an initial value; and a super-resolution reconstruction module configured for performing high-resolution reconstruction on the initial value. According to the present application, a parameter fine-tuning method is adopted, and a CT reconstruction network which is not suitable for a certain patient is adjusted into a network which is suitable for the patient's situation on the premise of not using a large number of data sets for training; only the low-resolution CT data of the patient is used in this process, and the corresponding high-resolution CT data is not needed as a label.


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