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:
Nov. 07, 2023

Filed:

Sep. 14, 2020
Applicants:

Huidong Xie, Troy, NY (US);

GE Wang, Loudonville, NY (US);

Hongming Shan, Troy, NY (US);

Wenxiang Cong, Albany, NY (US);

Inventors:

Huidong Xie, Troy, NY (US);

Ge Wang, Loudonville, NY (US);

Hongming Shan, Troy, NY (US);

Wenxiang Cong, Albany, NY (US);

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
A61B 6/03 (2006.01); G06T 11/00 (2006.01); A61B 6/00 (2006.01);
U.S. Cl.
CPC ...
A61B 6/032 (2013.01); A61B 6/5205 (2013.01); G06T 11/005 (2013.01); G06T 11/006 (2013.01); G06T 2211/421 (2013.01); G06T 2211/436 (2013.01);
Abstract

A system for few-view computed tomography (CT) image reconstruction is described. The system includes a preprocessing module, a first generator network, and a discriminator network. The preprocessing module is configured to apply a ramp filter to an input sinogram to yield a filtered sinogram. The first generator network is configured to receive the filtered sinogram, to learn a filtered back-projection operation and to provide a first reconstructed image as output. The first reconstructed image corresponds to the input sinogram. The discriminator network is configured to determine whether a received image corresponds to the first reconstructed image or a corresponding ground truth image. The generator network and the discriminator network correspond to a Wasserstein generative adversarial network (WGAN). The WGAN is optimized using an objective function based, at least in part, on a Wasserstein distance and based, at least in part, on a gradient penalty.


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