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 modular material decomposition from energy resolving photon counting data. According to an embodiment, a system is provided. The system can further comprise a processor that can execute computer-executable components stored in memory, wherein the computer-executable components can comprise a reconstruction component that can reconstruct one or more material decomposed CT images by balancing a forward model agent and a prior model agent, wherein the forward model agent can represent a conditional distribution of observed data given an unknown CT image, wherein the prior model agent can represent an assumed prior distribution, and wherein the observed data can include measurements from a PCD.

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: A method for measuring a sample to identify a chemical includes receiving respective spectra for each of a plurality of chemicals. Using a processor, a plurality of binary mathematical filters are computed using the received spectra. A spatial light modulator is adjusted according to a selected mathematical filter. Light that has interacted with the sample is dispersed over the surface of the spatial light modulator, so that the spatial light modulator provides light at wavelengths corresponding to the selected mathematical filter. The light provided by the spatial light modulator is measured to provide a score corresponding to the selected mathematical filter. Filter scores are combined to determine a chemical amount. The processor can operate detection apparatus having a light source, an objective for focusing source light onto the sample, a spatial light modulator, and a detector for detecting the modulator output.

Abstract: A method for measuring a sample to identify a chemical includes receiving respective spectra for each of a plurality of chemicals. Using a processor, a plurality of binary mathematical filters are computed using the received spectra. A spatial light modulator is adjusted according to a selected mathematical filter. Light that has interacted with the sample is dispersed over the surface of the spatial light modulator, so that the spatial light modulator provides light at wavelengths corresponding to the selected mathematical filter. The light provided by the spatial light modulator is measured to provide a score corresponding to the selected mathematical filter. Filter scores are combined to determine a chemical amount. The processor can operate detection apparatus having a light source, an objective for focusing source light onto the sample, a spatial light modulator, and a detector for detecting the modulator output.