# Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution

arXiv: https://arxiv.org/abs/2404.04785 · HTML: https://mlsanity.com/paper/2404.04785/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 27 of its 48 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 27 of 27)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [Going deeper with Image Transformers](https://mlsanity.com/paper/2103.17239/) (2021)
- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [Score-based diffusion models for accelerated MRI](https://mlsanity.com/paper/2110.05243/) (2021)
- [DiffIR: Efficient Diffusion Model for Image Restoration](https://mlsanity.com/paper/2303.09472/) (2023)
- [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022)
- [Adaptive Diffusion Priors for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2207.05876/) (2022)
- [Hierarchical Integration Diffusion Model for Realistic Image Deblurring](https://mlsanity.com/paper/2305.12966/) (2023)
- [MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution](https://mlsanity.com/paper/2106.02299/) (2021)
- [Diffusion Probabilistic Models beat GANs on Medical Images](https://mlsanity.com/paper/2212.07501/) (2022)
- [Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction](https://mlsanity.com/paper/2203.03623/) (2022)
- [CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis](https://mlsanity.com/paper/2303.14081/) (2023)
- [Towards performant and reliable undersampled MR reconstruction via diffusion model sampling](https://mlsanity.com/paper/2203.04292/) (2022)
- [SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation](https://mlsanity.com/paper/2212.08228/) (2022)
- [Multi-Contrast Super-Resolution MRI Through a Progressive Network](https://mlsanity.com/paper/1908.01612/) (2019)
- [Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration Network](https://mlsanity.com/paper/2105.08949/) (2021)
- [Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution](https://mlsanity.com/paper/2203.13963/) (2022)
- [DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2303.13933/) (2023)
- [InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model](https://mlsanity.com/paper/2308.12465/) (2023)
- [SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolution](https://mlsanity.com/paper/2303.09735/) (2023)
- [Semi-Implicit Denoising Diffusion Models (SIDDMs)](https://mlsanity.com/paper/2306.12511/) (2023)
- [High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss](https://mlsanity.com/paper/2107.09989/) (2021)

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

- [Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt](https://mlsanity.com/paper/2404.11474/) (2024)
- [Incomplete Multi-view Clustering via Diffusion Contrastive Generation](https://mlsanity.com/paper/2503.09185/) (2025)
- [Rethinking Video Deblurring with Wavelet-Aware Dynamic Transformer and Diffusion Model](https://mlsanity.com/paper/2408.13459/) (2024)
- [Frequency Domain-Based Diffusion Model for Unpaired Image Dehazing](https://mlsanity.com/paper/2507.01275/) (2025)
- [MRI Super-Resolution with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2511.16854/) (2025)
- [Diffusion Models for Computational Neuroimaging: A Survey](https://mlsanity.com/paper/2502.06552/) (2025)
- [BlurDM: A Blur Diffusion Model for Image Deblurring](https://mlsanity.com/paper/2512.03979/) (2025)
- [Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI](https://mlsanity.com/paper/2405.14327/) (2024)
- [Rethink Arbitrary Style Transfer with Transformer and Contrastive Learning](https://mlsanity.com/paper/2404.13584/) (2024)
- [DyArtbank: Diverse Artistic Style Transfer via Pre-trained Stable Diffusion and Dynamic Style Prompt Artbank](https://mlsanity.com/paper/2503.08392/) (2025)
- [EquiReg: Equivariance Regularized Diffusion for Inverse Problems](https://mlsanity.com/paper/2505.22973/) (2025)
- [Sub-DM:Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstruction](https://mlsanity.com/paper/2411.03758/) (2024)
- [Learning Deblurring Texture Prior from Unpaired Data with Diffusion Model](https://mlsanity.com/paper/2507.13599/) (2025)
- [SSCM: A Spatial-Semantic Consistent Model for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2509.18593/) (2025)
- [Temporal and Spatial Super Resolution with Latent Diffusion Model in Medical MRI images](https://mlsanity.com/paper/2410.23898/) (2024)
- [From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction](https://mlsanity.com/paper/2506.16210/) (2025)
- [MDPG: Multi-domain Diffusion Prior Guidance for MRI Reconstruction](https://mlsanity.com/paper/2506.23701/) (2025)
- [Lesion-Aware Post-Training of Latent Diffusion Models for Synthesizing Diffusion MRI from CT Perfusion](https://mlsanity.com/paper/2510.09056/) (2025)
- [Equivariant Sampling for Improving Diffusion Model-based Image Restoration](https://mlsanity.com/paper/2511.09965/) (2025)
- [A Unified Framework for Multimodal Image Reconstruction and Synthesis using Denoising Diffusion Models](https://mlsanity.com/paper/2602.08249/) (2026)
- [HNDiff: Haze-Noise Diffusion for Image Dehazing](https://mlsanity.com/paper/2608.10995/) (2026)
- [Rapid Whole Brain Motion-robust Mesoscale In-vivo MR Imaging using Multi-scale Implicit Neural Representation](https://mlsanity.com/paper/2502.08634/) (2025)
- [Deterministic Medical Image Translation via High-fidelity Brownian Bridges](https://mlsanity.com/paper/2503.22531/) (2025)
- [Decoupling Multi-Contrast Super-Resolution: Self-Supervised Implicit Re-Representation for Unpaired Cross-Modal Synthesis](https://mlsanity.com/paper/2505.05855/) (2025)
- [CD-DPE: Dual-Prompt Expert Network Based on Convolutional Dictionary Feature Decoupling for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2511.14014/) (2025)
- [ReBrain: Brain MRI Reconstruction from Sparse CT Slice via Retrieval-Augmented Diffusion](https://mlsanity.com/paper/2511.17068/) (2025)
- [Solving a Nonlinear Blind Inverse Problem for Tagged MRI with Physics and Deep Generative Priors](https://mlsanity.com/paper/2603.00882/) (2026)
- [PhyMRI-SR: Toward Physics-Aware MRI Image Super-Resolution](https://mlsanity.com/paper/2607.06238/) (2026)
- [MIAR: Medical Image Super-Resolution With Autoregressive Modeling](https://mlsanity.com/paper/2609.26103/) (2026)
- [VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution](https://mlsanity.com/paper/2610.20509/) (2026)

## Abstract (arXiv)

Recently, diffusion models (DM) have been applied in magnetic resonance imaging (MRI) super-resolution (SR) reconstruction, exhibiting impressive performance, especially with regard to detailed reconstruction. However, the current DM-based SR reconstruction methods still face the following issues: (1) They require a large number of iterations to reconstruct the final image, which is inefficient and consumes a significant amount of computational resources. (2) The results reconstructed by these methods are often misaligned with the real high-resolution images, leading to remarkable distortion in the reconstructed MR images. To address the aforementioned issues, we propose an efficient diffusion model for multi-contrast MRI SR, named as DiffMSR. Specifically, we apply DM in a highly compact low-dimensional latent space to generate prior knowledge with high-frequency detail information. The highly compact latent space ensures that DM requires only a few simple iterations to produce accurate prior knowledge. In addition, we design the Prior-Guide Large Window Transformer (PLWformer) as the decoder for DM, which can extend the receptive field while fully utilizing the prior knowledge generated by DM to ensure that the reconstructed MR image remains undistorted. Extensive experiments on public and clinical datasets demonstrate that our DiffMSR outperforms state-of-the-art methods.