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arXiv · 2606.01784

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

Yuyang Li·Yipin Deng·Wenlei Shang·Juncen Wu·Xin Bai·Zijian Zhou·Peng Hu
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Abstract

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.

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Yuyang Li, Yipin Deng, Wenlei Shang, Juncen Wu, Xin Bai, Zijian Zhou, Peng Hu. 2026-06-01. MoRE: A Mixture-of-Experts-Based Task-Adaptive End-to-End Network for Multimodal MRI Reconstruction. https://arxiv.org/abs/2606.01784

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