Enhance Generative Adversarial Networks By Wavelet Transform To Denoise Low-Dose Ct Images

Computed Tomography (CT) has been widely used in clinical diagnosis, while its potential risk of X-ray radiation has attracted serious public concerns. Reconstructing high-quality images from low-dose CT devices is a promising solution. Whereas, existing methods mostly relied on the raw data of devices, and cannot be shared among different device suppliers. Inspired by the powerful learning ability of GAN and the structural information extraction ability of wavelet transform, we propose to combine the two together and design the WT-GAN, which extracts structure and noise information by wavelet transform and generates high-quality images by GAN. The two technologies are incorporated with each other by our well-designed loss functions. Experimental results show that the proposed WT-GAN achieves superior performance and can efficiently extract the noise while retaining the texture details. Furthermore, the WT-GAN is a postprocessing method imposed on full-size images, thus it is easy to integrate into any CT systems.