Coupled-Projection Residual Network for MRI Super-Resolution

TL;DR

This paper introduces a novel Coupled-Projection Residual Network (CPRN) that effectively enhances MRI image resolution by combining shallow and deep networks with innovative feedback and fusion mechanisms, outperforming existing methods.

Contribution

The paper proposes a new CPRN architecture with coupled-projection and residual learning, improving MRI super-resolution by better preserving details and learning high-frequency information.

Findings

CPRN outperforms state-of-the-art MRI super-resolution methods on three datasets.

The coupled-projection mechanism enhances detail retention in MRI images.

Feature fusion via step-wise connection improves reconstruction quality.

Abstract

Magnetic Resonance Imaging(MRI) has been widely used in clinical application and pathology research by helping doctors make more accurate diagnoses. On the other hand, accurate diagnosis by MRI remains a great challenge as images obtained via present MRI techniques usually have low resolutions. Improving MRI image quality and resolution thus becomes a critically important task. This paper presents an innovative Coupled-Projection Residual Network (CPRN) for MRI super-resolution. The CPRN consists of two complementary sub-networks: a shallow network and a deep network that keep the content consistency while learning high frequency differences between low-resolution and high-resolution images. The shallow sub-network employs coupled-projection for better retaining the MRI image details, where a novel feedback mechanism is introduced to guide the reconstruction of high-resolution images. The deep sub-network learns from the residuals of the high-frequency image information, where multiple residual blocks are cascaded to magnify the MRI images at the last network layer. Finally, the features from the shallow and deep sub-networks are fused for the reconstruction of high-resolution MRI images. For effective fusion of features from the deep and shallow sub-networks, a step-wise connection (CPRN S) is designed as inspired by the human cognitive processes (from simple to complex). Experiments over three public MRI datasets show that our proposed CPRN achieves superior MRI super-resolution performance as compared with the state-of-the-art. Our source code will be publicly available at http://www.yongxu.org/lunwen.html.