CT super-resolution GAN constrained by the identical, residual and cycle learning ensemble (GAN-circle)

A method for generating a high resolution (HR) computed tomography (CT) image from a low resolution (LR) CT image, the method comprising:

• generating, by a first generative neural network (G), a corresponding estimated HR image dataset based, at least in part, on a received training LR image dataset;
• comparing, by a first discriminative neural network (DY), a training HR image dataset and the estimated HR image dataset, the first generative neural network (G) and the first discriminative neural network (DY) included in a first generative adversarial network (GAN);
• generating, by a second generative neural network (F), a corresponding estimated LR image dataset based, at least in part, on the training HR image dataset;
• comparing, by a second discriminative neural network (DX), the training LR image dataset and the estimated LR image dataset, the second generative neural network (F) and the second discriminative neural network (DX) included in a second GAN;
• determining, by an optimization module, an optimization function based, at least in part, on at least one of the estimated HR image dataset and/or the estimated LR image dataset, the optimization function containing at least one loss function;
• and
• adjusting, by the optimization module, a plurality of neural network parameters associated with at least one of the first GAN and/or the second GAN, to optimize the optimization function.

A computer readable storage device having stored thereon instructions configured to generate a high resolution (HR) computed tomography (CT) image from a low resolution (LR) CT image, the instructions that when executed by one or more processors result in the following operations comprising:

• generating, by a first generative neural network (G), a corresponding estimated HR image dataset based, at least in part, on a received training LR image dataset;
• comparing, by a first discriminative neural network (DY), a training HR image dataset and the estimated HR image dataset, the first generative neural network (G) and the first discriminative neural network (DY) included in a first generative adversarial network (GAN);
• generating, by a second generative neural network (F), a corresponding estimated LR image dataset based, at least in part, on the training HR image dataset;
• comparing, by a second discriminative neural network (DX), the training LR image dataset and the estimated LR image dataset, the second generative neural network (F) and the second discriminative neural network (DX) included in a second GAN;
• determining, by an optimization module, an optimization function based, at least in part, on at least one of the estimated HR image dataset and/or the estimated LR image dataset, the optimization function containing at least one loss function;
• and
• adjusting, by the optimization module, a plurality of neural network parameters associated with at least one of the first GAN and/or the second GAN, to optimize the optimization function.