Brain MR Images Super-Resolution with the Consistent Features

Abstract

Magnetic resonance imaging plays an important role in auxiliary diagnosis and brain exploration. However, limited by hardware, scanning time and cost, it's challenging to acquire high-resolution (HR) magnetic resonance (MR) image clinically. In this paper, consistent feature generative adversarial network (CFGAN) is proposed to produce HR MR images from the low-resolution counterparts. Specifically, a consistent-features encoder is employed to extract the multi-scales features and encode them into latent codes. Then, a progressive generator is utilized to decode the latent codes from high-level to low-level features. With the encoder and generator, the shared consistent features between low-resolution and high-resolution can be fully extracted and recovered. Experiments on ADNI dataset demonstrate that CFGAN outperforms the competing methods quantitatively and qualitatively.