Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to image enhancement using a generative adversarial network (GAN). The computer-implemented system can comprise a memory that can store computer-executable components. The computer-implemented system can further comprise a processor that can execute the computer-executable components stored in the memory, wherein the computer-executable components can comprise a training component that can train a discriminator of the GAN to score a texture of a CT image, wherein the texture can be derived from a difference of two conditionally independent estimates produced by a generator of the GAN by respectively processing two independent noisy samples of images.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to image enhancement using a generative adversarial network (GAN). The computer-implemented system can comprise a memory that can store computer-executable components. The computer-implemented system can further comprise a processor that can execute the computer-executable components stored in the memory, wherein the computer-executable components can comprise a training component that can train a discriminator of the GAN to score a texture of a CT image, wherein the texture can be derived from a difference of two conditionally independent estimates produced by a generator of the GAN by respectively processing two independent noisy samples of images.

Abstract: Various methods and systems are provided for estimating and compensating for table deflection in reconstructed images. In one embodiment, a method for computed tomography (CT) imaging comprises reconstructing images from data acquired during a helical CT scan where table deflection parameters are estimated and the reconstruction is adjusted based on the table deflection parameters. In this way, images may be reconstructed without artifacts caused by table deflection.

Abstract: Various methods and systems are provided for estimating and compensating for table deflection in reconstructed images. In one embodiment, a method for computed tomography (CT) imaging comprises reconstructing images from data acquired during a helical CT scan where table deflection parameters are estimated and the reconstruction is adjusted based on the table deflection parameters. In this way, images may be reconstructed without artifacts caused by table deflection.