用可证明收敛的优化网络，统一解决多线圈与多模态磁共振成像重建问题。
Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimization
- 将自适应前向后向算法展开为神经网络，融合数据保真与非凸正则化
- 在极端欠采样下比传统监督学习提升显著，PSNR与SSIM均更优
- 适合临床中采样模式和模态组合多变的场景，泛化能力强
我们提出一种统一的深度元学习框架，用于加速磁共振成像（MRI），同时处理多线圈重建与跨模态合成。针对传统方法在欠采样数据和缺失模态下的局限性，本方法将一个可证明收敛的优化算法展开为结构化神经网络。网络每一步模拟带外推的自适应前向后向迭代，以合理方式融入数据保真项与非凸正则化。为增强不同采集设置下的泛化能力，引入元学习机制，使模型能利用任务特定元知识快速适应未见采样模式和模态组合。在开源数据集上的评估显示，该方法在极端欠采样及域偏移条件下，相较于传统监督学习，在PSNR与SSIM上均有显著提升。结果表明，展开优化、任务感知元学习与模态融合的协同效应，提供了一种可扩展且通用的临床真实磁共振重建方案。
原文摘要 · Abstract (English)
We propose a unified deep meta-learning framework for accelerated magnetic resonance imaging (MRI) that jointly addresses multi-coil reconstruction and cross-modality synthesis. Motivated by the limitations of conventional methods in handling undersampled data and missing modalities, our approach unrolls a provably convergent optimization algorithm into a structured neural network architecture. Each phase of the network mimics a step of an adaptive forward-backward scheme with extrapolation, enabling the model to incorporate both data fidelity and nonconvex regularization in a principled manner. To enhance generalization across different acquisition settings, we integrate meta-learning, which enables the model to rapidly adapt to unseen sampling patterns and modality combinations using task-specific meta-knowledge. The proposed method is evaluated on the open source datasets, showing significant improvements in PSNR and SSIM over conventional supervised learning, especially under aggressive undersampling and domain shifts. Our results demonstrate the synergy of unrolled optimization, task-aware meta-learning, and modality fusion, offering a scalable and generalizable solution for real-world clinical MRI reconstruction.
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