Dense channel splitting network for MR image super-resolution

Abstract

High spatial resolution is desirable in magnetic resonance imaging (MRI) as it can provide detailed anatomical information, facilitating radiologists with accurate quantitative analysis. Super-resolution (SR) algorithms are effective approaches to enhance MR images' spatial resolution. In the past few years, convolutional neural network (CNN)-based SR methods have significantly improved and outperformed conventional ones. However, existing CNN-based SR methods usually do not explicitly consider the frequency property of images, leading to the limited representation of high-frequency components reflecting image details. To alleviate this problem, a dense channel splitting network (DCSN) algorithm is proposed to process the frequency bands for better feature detection. Specifically, a channel splitting module, a cascaded multi-branch dilation module, and a dense-in and recursive-out mechanism are designed to separate frequency bands of MR images and forward the high-frequency information to deeper layers for reconstruction. Several experiments are performed on real T2 brain and PD (proton density) knee images. The results indicate that the proposed network is superior to conventional CNN-based SR methods.

Keywords: Convolutional neural network; Machine learning; Magnetic resonance imaging; Super-resolution.