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. 25, 2022

Filed:

Sep. 30, 2020
Applicant:

Zhejiang University, Hangzhou, CN;

Inventors:

Huafeng Liu, Hangzhou, CN;

Bo Wang, Hangzhou, CN;

Assignee:

ZHEJIANG UNIVERSITY, Hangzhou, CN;

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 11/00 (2006.01); G06T 5/00 (2006.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); A61B 6/00 (2006.01); A61B 6/03 (2006.01);
U.S. Cl.
CPC ...
G06T 11/008 (2013.01); A61B 6/037 (2013.01); A61B 6/4241 (2013.01); G06N 3/04 (2013.01); G06N 3/084 (2013.01); G06T 5/002 (2013.01); G06T 7/0012 (2013.01); G06T 11/006 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01);
Abstract

A PET image reconstruction method, including: 1) injecting a PET radioactive tracer into a biological tissue, scanning by a PET device, and detecting and counting coincidence photons to obtain an original protection data matrix; 2) establishing a measurement equation model; 3) splitting the reconstruction problem into a first sub-problem and a second sub-problem; 4) solving the first sub-problem by a filtered back-projection layer, solving the second sub-problem by an improved denoising convolutional neural network, where the filtered back-projection layer and the improved denoising convolutional neural network are connected in series to form a filtered back-projection network (FBP-Net); 5) inputting original projection data into the FBP-Net, and using an image as a tag to adjust parameters of the FBP-Net to reduce an error between an output of the FBP-Net and the tag; and 6) inputting projection data to be reconstructed into the trained FBP-Net to obtain a desired reconstructed image.


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