A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction

TL;DR

This paper introduces a novel deep learning model for multi-contrast MRI reconstruction that leverages feature sharing and dense connections to improve accuracy, efficiency, and robustness over traditional methods.

Contribution

The paper presents the first deep learning approach for multi-contrast MRI reconstruction using feature sharing units and dense inference blocks, reducing parameters and enhancing performance.

Findings

Outperforms state-of-the-art methods in accuracy and efficiency.

Demonstrates robustness in non-registration environments.

Improves reconstruction quality for better medical image analysis.

Abstract

In multi-contrast magnetic resonance imaging (MRI), compressed sensing theory can accelerate imaging by sampling fewer measurements within each contrast. The conventional optimization-based models suffer several limitations: strict assumption of shared sparse support, time-consuming optimization and "shallow" models with difficulties in encoding the rich patterns hiding in massive MRI data. In this paper, we propose the first deep learning model for multi-contrast MRI reconstruction. We achieve information sharing through feature sharing units, which significantly reduces the number of parameters. The feature sharing unit is combined with a data fidelity unit to comprise an inference block. These inference blocks are cascaded with dense connections, which allows for information transmission across different depths of the network efficiently. Our extensive experiments on various multi-contrast MRI datasets show that proposed model outperforms both state-of-the-art single-contrast and multi-contrast MRI methods in accuracy and efficiency. We show the improved reconstruction quality can bring great benefits for the later medical image analysis stage. Furthermore, the robustness of the proposed model to the non-registration environment shows its potential in real MRI applications.