Texture Matching GAN for CT Image Enhancement
This work addresses texture issues in CT image enhancement for clinical applications, representing an incremental improvement over existing GAN-based methods.
The paper tackles the problem of undesirable or unrealistic texture in CT image enhancement using deep neural networks and GANs, proposing a texture matching GAN (TMGAN) that separates anatomical features from generated texture to match target textures, resulting in enhanced image quality with clinically desirable texture.
Deep neural networks (DNN) are commonly used to denoise and sharpen X-ray computed tomography (CT) images with the goal of reducing patient X-ray dosage while maintaining reconstruction quality. However, naive application of DNN-based methods can result in image texture that is undesirable in clinical applications. Alternatively, generative adversarial network (GAN) based methods can produce appropriate texture, but naive application of GANs can introduce inaccurate or even unreal image detail. In this paper, we propose a texture matching generative adversarial network (TMGAN) that enhances CT images while generating an image texture that can be matched to a target texture. We use parallel generators to separate anatomical features from the generated texture, which allows the GAN to be trained to match the desired texture without directly affecting the underlying CT image. We demonstrate that TMGAN generates enhanced image quality while also producing image texture that is desirable for clinical application.