Generative adversarial network-based lossless image compression model for cross-sectional imaging

Abstract

A novel medical image generation approach synthesizes thin-cut computerized tomography (CT) images from thick-cut CT ones as inputs. First thick-cut CT images are obtained by maximizing the pixel-wise intensity of five or more than five continuous thin-cut ones after image registration. Second, the obtained thick-cut CT images are fed into a generator block which adopts an encoder-decoder architecture, where each thick-cut image is encoded into low-dimensional embedding space before decoding into multiple thin-cut ones. Third, a discriminator focuses on distinguishing original real thin-cut images from synthetic thin-cut images. An adversarial mechanism between the generator and discriminator causes the discriminator's output to provide an effective gradient update of the network parameters for the generator to increasingly improve the generator's ability to synthesize higher-quality thin-cut images and in turn promotes the discriminator's discriminating capability.