用专家混合模型提升多模态MRI重建，计算量小却效果好
MoRE: A Mixture-of-Experts-Based Task-Adaptive End-to-End Network for Multimodal MRI Reconstruction
- 采用稀疏激活的专家混合模块，按样本自动选择最优解码器
- 8倍下采样下在脑与膝关节数据上保持稳定高保真度重建
- 专家分工明确可解释，适合资源受限的临床医学图像重建
尽管加速MRI重建通过端到端学习取得显著进展，但在计算资源受限的情况下，构建一个能跨不同解剖结构和对比度通用的单一网络仍具挑战。本文提出MoRE，一种集成于端到端变分网络中的稀疏激活专家混合（MoE）模块。MoRE结合共享编码器与样本自适应的无监督路由机制，仅激活少量专家解码器，同时严格保持基于物理的数据一致性。在fastMRI多线圈脑部与膝部数据集上，8倍下采样条件下，MoRE在多种对比度数据上均实现稳定的高结构相似性（SSIM）与峰值信噪比（PSNR）。此外，路由嵌入的t-SNE可视化揭示了可解释的、模态感知的专家专化现象。稀疏条件计算机制确保架构开销适中。结果表明，MoE式容量扩展可在不显著增加计算成本的前提下，显著提升通用MRI重建性能。
原文摘要 · Abstract (English)
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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