提升心脏MRI重建效果，通过数据增强与轻量模型扩展。
Branch Learning in MRI: More Data, More Models, More Training
- 结合空域变换与相位噪声/运动模拟，保持物理一致性增强数据。
- 轻量提示词提升性能稳定，稀疏专家模型可继续优化但效率低。
- 适合追求高效高鲁棒性重建的医学影像研究者参考。
我们研究了两种互补策略以提升多对比度心脏MRI重建：物理一致的数据空间增强（DualSpaceCMR）和基于VQPrompt与Moero的参数高效容量扩展。DualSpaceCMR在图像层面引入变换，并结合k空间噪声与运动仿真，同时保持前向模型一致性。VQPrompt引入轻量瓶颈提示；Moero在深层非迭代网络中嵌入稀疏专家混合模型，采用基于直方图的路由机制。在多厂商、多中心的CMRxRecon25基准上评估少样本与分布外泛化能力。小数据集下，k空间运动+噪声增强有效；但在大规模基准上反而降低性能，揭示对增强比例与调度敏感。VQPrompt带来微小但稳定的提升，内存开销可忽略。Moero在早期平台后持续改进，且保持基线级的少样本与分布外表现，尽管存在轻微过拟合；但稀疏路由降低了PyTorch吞吐量，使实际运行时间成为主要瓶颈。结果表明需考虑规模的增强策略，提示词式容量扩展是可行路径，而稀疏专家模型的效率仍需优化。
原文摘要 · Abstract (English)
We investigated two complementary strategies for multicontrast cardiac MR reconstruction: physics-consistent data-space augmentation (DualSpaceCMR) and parameter-efficient capacity scaling via VQPrompt and Moero. DualSpaceCMR couples image-level transforms with kspace noise and motion simulations while preserving forwardmodel consistency. VQPrompt adds a lightweight bottleneck prompt; Moero embeds a sparse mixture of experts within a deep unrolled network with histogram-based routing. In the multivendor, multisite CMRxRecon25 benchmark, we evaluate fewshot and out-of-distribution generalization. On small datasets, k-space motion-plus-noise improves reconstruction; on the large benchmark it degrades performance, revealing sensitivity to augmentation ratio and schedule. VQPrompt produces modest and consistent gains with negligible memory overhead. Moero continues to improve after early plateaus and maintains baseline-like fewshot and out-of-distribution behavior despite mild overfitting, but sparse routing lowers PyTorch throughput and makes wall clock time the main bottleneck. These results motivate scale-aware augmentation and suggest prompt-based capacity scaling as a practical path, while efficiency improvements are crucial for sparse expert models.
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