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:
Sep. 19, 2023

Filed:

Aug. 06, 2019
Applicant:

Vanderbilt University, Nashville, TN (US);

Inventors:

Benoit M. Dawant, Nashville, TN (US);

Jianing Wang, Nashville, TN (US);

Jack H. Noble, Nashville, TN (US);

Robert F. Labadie, Nashville, TN (US);

Assignee:

VANDERBILT UNIVERSITY, Nashville, TN (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06T 11/00 (2006.01); G06V 10/25 (2022.01); G06T 3/00 (2006.01); G06T 7/00 (2017.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
G06T 11/008 (2013.01); G06N 3/045 (2023.01); G06T 3/0075 (2013.01); G06T 7/0012 (2013.01); G06V 10/25 (2022.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30052 (2013.01);
Abstract

A deep-learning-based method for metal artifact reduction in CT images includes providing a dataset and a cGAN. The dataset includes CT image pairs, randomly partitioned into a training set, a validation set, and a testing set. Each Pre-CT and Post-CT image pairs is respectively acquired in a region before and after an implant is implanted. The Pre-CT and Post-CT images of each pair are artifact-free CT and artifact-affected CT images, respectively. The cGAN is conditioned on the Post-CT images, includes a generator and a discriminator that operably compete with each other, and is characterized with a training objective that is a sum of an adversarial loss and a reconstruction loss. The method also includes training the cGAN with the dataset; inputting the post-operatively acquired CT image to the trained cGAN; and generating an artifact-corrected image by the trained cGAN, where metal artifacts are removed in the artifact-corrected image.


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