  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.
