# MoRE: A Mixture-of-Experts-Based Task-Adaptive End-to-End Network for Multimodal MRI Reconstruction

arXiv: https://arxiv.org/abs/2606.01784 · HTML: https://mlsanity.com/paper/2606.01784/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts.
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Placed in

- Mixture-of-experts / [Medical and biomedical MoE](https://mlsanity.com/moe/medical-and-biomedical-moe/)

## Builds on (most cited first, 12 of 12)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [End-to-End Variational Networks for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2004.06688/) (2020)
- [A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2503.07137/) (2025)
- [Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/1904.01112/) (2019)
- [Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learners](https://mlsanity.com/paper/2204.07689/) (2022)
- [The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023](https://mlsanity.com/paper/2404.01082/) (2024)
- [Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data](https://mlsanity.com/paper/2312.10271/) (2023)
- [Towards Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge](https://mlsanity.com/paper/2503.03971/) (2025)
- [Sparse Mixture-of-Experts for Non-Uniform Noise Reduction in MRI Images](https://mlsanity.com/paper/2501.14198/) (2025)

## Abstract (arXiv)

Although accelerated MRI reconstruction has advanced rapidly through end-to-end learning, deploying a single unified network that generalizes across diverse anatomies and contrasts under constrained computational resources remains challenging. In this paper, we introduce MoRE, a sparsely activated mixture-of-experts (MoE) module integrated into an end-to-end variational network. MoRE couples a shared encoder with sample-wise, unsupervised routing to activate a minimal subset of expert decoders while strictly preserving physics-based data consistency. Evaluated on the fastMRI multi-coil brain and knee datasets under 8x undersampling, MoRE achieves highly stable SSIM and PSNR performance across multi-contrast datasets. Furthermore, t-SNE visualization of the routing embeddings reveals interpretable, modality-aware expert specialization. The sparse conditional computation mechanism ensures that the architectural overhead remains modest. These results demonstrate that MoE-style capacity scaling can significantly enhance general-purpose MRI reconstruction without requiring proportional increases in computational power.