US 11,854,160 B2
CT super-resolution GAN constrained by the identical, residual and cycle learning ensemble (GAN-circle)
Ge Wang, Loudonville, NY (US); Chenyu You, Stanford, CA (US); Wenxiang Cong, Albany, NY (US); and Hongming Shan, Troy, NY (US)
Assigned to Rensselaer Polytechnic Institute, Troy, NY (US)
Filed by RENSSELAER POLYTECHNIC INSTITUTE, Troy, NY (US)
Filed on Dec. 29, 2021, as Appl. No. 17/564,728.
Application 17/564,728 is a continuation of application No. 16/594,567, filed on Oct. 7, 2019, granted, now 11,232,541.
Claims priority of provisional application 62/910,703, filed on Oct. 4, 2019.
Claims priority of provisional application 62/742,586, filed on Oct. 8, 2018.
Prior Publication US 2022/0230278 A1, Jul. 21, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 3/40 (2006.01); G06N 3/045 (2023.01)
CPC G06T 3/4076 (2013.01) [G06N 3/045 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A system for generating a high resolution (HR) computed tomography (CT) image from a low resolution (LR) CT image, the system comprising:
a first generative adversarial network (GAN) comprising:
a first generative neural network (G) configured to generate an estimated HR image based, at least in part, on a received LR image, and
a first discriminative neural network (DY) configured to compare the estimated HR image and a received training HR image; and
an optimization module configured to determine an optimization function based, at least in part, on the estimated HR image, the optimization function containing at least one loss function, the optimization module further configured to adjust a plurality of neural network parameters associated with the first GAN, to optimize the optimization function.