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:
May. 20, 2025

Filed:

Aug. 19, 2022
Applicant:

Shanghai United Imaging Intelligence Co., Ltd., Shanghai, CN;

Inventors:

Zhang Chen, Cambridge, MA (US);

Siyuan Dong, New Haven, CT (US);

Shanhui Sun, Cambridge, MA (US);

Xiao Chen, Cambridge, MA (US);

Yikang Liu, Cambridge, MA (US);

Terrence Chen, Lexington, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 5/70 (2024.01); G06T 5/10 (2006.01); G06T 5/20 (2006.01); G06T 5/73 (2024.01); G06T 7/00 (2017.01); G06T 11/00 (2006.01);
U.S. Cl.
CPC ...
G06T 11/008 (2013.01); G06T 5/10 (2013.01); G06T 5/20 (2013.01); G06T 5/70 (2024.01); G06T 5/73 (2024.01); G06T 7/0014 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30008 (2013.01);
Abstract

Described herein are systems, methods, and instrumentalities associated with using an invertible neural network to complete various medical imaging tasks. Unlike traditional neural networks that may learn to map input data (e.g., a blurry reconstructed MRI image) to ground truth (e.g., a fully-sampled MRI image), the invertible neural network may be trained to learn a mapping from the ground truth to the input data, and may subsequently apply an inverse of the mapping (e.g., at an inference time) to complete a medical imaging task. The medical imaging task may include, for example, MRI image reconstruction (e.g., to increase the sharpness of a reconstructed MRI image), image denoising, image super-resolution, and/or the like.


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