Noise Entangled GAN For Low-Dose CT Simulation
This work addresses the need for generating low-dose CT images without requiring proprietary scanner data, which is incremental as it builds on GAN-based methods for medical imaging simulation.
The authors tackled the problem of simulating low-dose CT images from high-dose CT images by proposing a Noise Entangled GAN (NE-GAN), which can generate realistic low-dose CT images with controllable noise levels, as demonstrated in experimental results.
We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image. First, we present two schemes to generate a clean CT image and a noise image from the high-dose CT image. Then, given these generated images, an NE-GAN is proposed to simulate different levels of low-dose CT images, where the level of generated noise can be continuously controlled by a noise factor. NE-GAN consists of a generator and a set of discriminators, and the number of discriminators is determined by the number of noise levels during training. Compared with the traditional methods based on the projection data that are usually unavailable in real applications, NE-GAN can directly learn from the real and/or simulated CT images and may create low-dose CT images quickly without the need of raw data or other proprietary CT scanner information. The experimental results show that the proposed method has the potential to simulate realistic low-dose CT images.