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

arXiv: https://arxiv.org/abs/2105.08949 · HTML: https://mlsanity.com/paper/2105.08949/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 9 of 9)

- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks](https://mlsanity.com/paper/1801.02728/) (2018)
- [Multi-Contrast Super-Resolution MRI Through a Progressive Network](https://mlsanity.com/paper/1908.01612/) (2019)
- [Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction](https://mlsanity.com/paper/2104.05345/) (2021)
- [Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging](https://mlsanity.com/paper/1703.00035/) (2017)

## Built on by (most cited first, 30 of 42)

- [Task Transformer Network for Joint MRI Reconstruction and Super-Resolution](https://mlsanity.com/paper/2106.06742/) (2021)
- [Multi-Modal Transformer for Accelerated MR Imaging](https://mlsanity.com/paper/2106.14248/) (2021)
- [Learning Federated Visual Prompt in Null Space for MRI Reconstruction](https://mlsanity.com/paper/2303.16181/) (2023)
- [Specificity-Preserving Federated Learning for MR Image Reconstruction](https://mlsanity.com/paper/2112.05752/) (2021)
- [Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations](https://mlsanity.com/paper/2303.15065/) (2023)
- [Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution](https://mlsanity.com/paper/2203.13963/) (2022)
- [Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2404.04785/) (2024)
- [DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2303.13933/) (2023)
- [Memory-augmented Deep Unfolding Network for Guided Image Super-resolution](https://mlsanity.com/paper/2203.04960/) (2022)
- [Cross-Modality High-Frequency Transformer for MR Image Super-Resolution](https://mlsanity.com/paper/2203.15314/) (2022)
- [SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolution](https://mlsanity.com/paper/2303.09735/) (2023)
- [Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution](https://mlsanity.com/paper/2204.04218/) (2022)
- [MMR-Mamba: Multi-Modal MRI Reconstruction with Mamba and Spatial-Frequency Information Fusion](https://mlsanity.com/paper/2406.18950/) (2024)
- [Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution](https://mlsanity.com/paper/2109.01664/) (2021)
- [TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-Resolution](https://mlsanity.com/paper/2306.06669/) (2023)
- [Score-based Self-supervised MRI Denoising](https://mlsanity.com/paper/2505.05631/) (2025)
- [PARCEL: Physics-based Unsupervised Contrastive Representation Learning for Multi-coil MR Imaging](https://mlsanity.com/paper/2202.01494/) (2022)
- [MRI Super-Resolution with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2511.16854/) (2025)
- [Deep Unfolding Convolutional Dictionary Model for Multi-Contrast MRI Super-resolution and Reconstruction](https://mlsanity.com/paper/2309.01171/) (2023)
- [Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction](https://mlsanity.com/paper/2207.11882/) (2022)
- [Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction](https://mlsanity.com/paper/2305.02774/) (2023)
- [Performance of a GPU- and Time-Efficient Pseudo 3D Network for Magnetic Resonance Image Super-Resolution and Motion Artifact Reduction](https://mlsanity.com/paper/2111.14259/) (2021)
- [Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation](https://mlsanity.com/paper/2205.06891/) (2022)
- [Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction](https://mlsanity.com/paper/2209.07030/) (2022)
- [Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual Learning](https://mlsanity.com/paper/2409.14113/) (2024)
- [Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution](https://mlsanity.com/paper/2504.10105/) (2025)
- [SSCM: A Spatial-Semantic Consistent Model for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2509.18593/) (2025)
- [Improved Super Resolution of MR Images Using CNNs and Vision Transformers](https://mlsanity.com/paper/2207.11748/) (2022)
- [Dual Arbitrary Scale Super-Resolution for Multi-Contrast MRI](https://mlsanity.com/paper/2307.02334/) (2023)
- [SGSR: Structure-Guided Multi-Contrast MRI Super-Resolution via Spatio-Frequency Co-Query Attention](https://mlsanity.com/paper/2408.03194/) (2024)

## Abstract (arXiv)

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.