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:
Dec. 02, 2025

Filed:

Aug. 16, 2021
Applicant:

GE Precision Healthcare Llc, Milwaukee, WI (US);

Inventors:

Rajesh Veera Venkata Lakshmi Langoju, Bengaluru, IN;

Utkarsh Agrawal, Bengaluru, IN;

Bipul Das, Chennai, IN;

Risa Shigemasa, Tokyo, JP;

Yasuhiro Imai, Tokyo, JP;

Jiang Hsieh, Brookfield, WI (US);

Assignee:

GE PRECISION HEALTHCARE LLC, Waukesha, WI (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 5/73 (2024.01); G06T 3/4046 (2024.01); G06T 3/4053 (2024.01); G06T 5/50 (2006.01);
U.S. Cl.
CPC ...
G06T 5/73 (2024.01); G06T 3/4046 (2013.01); G06T 3/4053 (2013.01); G06T 5/50 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01);
Abstract

Techniques are described for enhancing the quality of three-dimensional (3D) anatomy scan images using deep learning. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a reception component that receives a scan image generated from 3D scan data relative to a first axis of a 3D volume, and an enhancement component that applies an enhancement model to the scan image to generate an enhanced scan image having a higher resolution relative to the scan image. The enhancement model comprises a deep learning neural network model trained on training image pairs respectively comprising a low-resolution scan image and a corresponding high-resolution scan image respectively generated relative to a second axis of the 3D volume.


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