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. 31, 2023

Filed:

Dec. 17, 2021
Applicant:

Canon Medical Systems Corporation, Otawara, JP;

Inventors:

Chung Chan, Vernon Hills, IL (US);

Jian Zhou, Vernon Hills, IL (US);

Evren Asma, Vernon Hills, IL (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 6/00 (2006.01); A61B 6/03 (2006.01); G06N 3/08 (2023.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
A61B 6/5258 (2013.01); A61B 6/032 (2013.01); A61B 6/037 (2013.01); G06N 3/08 (2013.01); G06T 7/0012 (2013.01); G06T 2207/10004 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A deep learning (DL) convolution neural network (CNN) reduces noise in positron emission tomography (PET) images, and is trained using a range of noise levels for the low-quality images having high noise in the training dataset to produce uniform high-quality images having low noise, independently of the noise level of the input image. The DL-CNN network can be implemented by slicing a three-dimensional (3D) PET image into 2D slices along transaxial, coronal, and sagittal planes, using three separate 2D CNN networks for each respective plane, and averaging the outputs from these three separate 2D CNN networks. Feature-oriented training can be implemented by segmenting each training image into lesion and background regions, and, in the loss function, applying greater weights to voxels in the lesion region. Other medical images (e.g. MRI and CT) can be used to enhance resolution of the PET images and provide partial volume corrections.


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