Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration Network

TL;DR

This paper introduces MINet, a multi-stage integration network that explicitly models the complex relationships among multi-contrast MRI images to significantly enhance super-resolution quality.

Contribution

The paper proposes a novel multi-stage integration network that effectively captures inter-contrast dependencies for improved MRI super-resolution performance.

Findings

MINet outperforms existing methods on fastMRI and clinical datasets.

The multi-stage integration module effectively mines complex multi-contrast interactions.

Enhanced image quality demonstrated through various metrics.

Abstract

Super-resolution (SR) plays a crucial role in improving the image quality of magnetic resonance imaging (MRI). MRI produces multi-contrast images and can provide a clear display of soft tissues. However, current super-resolution methods only employ a single contrast, or use a simple multi-contrast fusion mechanism, ignoring the rich relations among different contrasts, which are valuable for improving SR. In this work, we propose a multi-stage integration network (i.e., MINet) for multi-contrast MRI SR, which explicitly models the dependencies between multi-contrast images at different stages to guide image SR. In particular, our MINet first learns a hierarchical feature representation from multiple convolutional stages for each of different-contrast image. Subsequently, we introduce a multi-stage integration module to mine the comprehensive relations between the representations of the multi-contrast images. Specifically, the module matches each representation with all other features, which are integrated in terms of their similarities to obtain an enriched representation. Extensive experiments on fastMRI and real-world clinical datasets demonstrate that 1) our MINet outperforms state-of-the-art multi-contrast SR methods in terms of various metrics and 2) our multi-stage integration module is able to excavate complex interactions among multi-contrast features at different stages, leading to improved target-image quality.