SO-Mamba：用于展开MRI重建的态所有权Mamba
SO-Mamba: State-Ownership Mamba for Unrolled MRI Reconstruction
AI总结：
本文提出SO-Mamba，一种用于展开MRI重建的态所有权Mamba正则化器，通过分配每个Mamba阶段的重建证据到递归驻留、态接口访问和非态输出校正，以提升重建质量与效率。
摘要：
加速MRI重建需要在大空间区域内恢复缺失细节的同时保持解剖学一致的结构。状态空间模型如Mamba提供高效的长距离建模，使其成为展开重建中的有吸引力的学得正则化器。然而，在数据一致性耦合的展开求解器中，不同阶段操作于不同的重建迭代，其中驻留载体应在不同阶段保持一致的重建内容，而阶段依赖的非驻留证据则与当前更新相关。将这些角色统一处理会将持久驻留载体证据和更新依赖的非驻留证据置于相同的递归内容路由中。为此，我们提出了SO-Mamba，一种态所有权Mamba正则化器，该正则化器将每个Mamba阶段的重建证据分配到递归驻留、态接口访问和非态输出校正。SO-Mamba通过State-Ownership Router (SOR)实现这一所有权规则，构建递归内容的驻留载体，并将非驻留证据路由到B/C态接口的仿射调制和输出校正出口。驻留载体提供Mamba内容路由，而非驻留证据流调整态接口并通过输出出口贡献，而无需进入递归内容路由。我们进一步引入了两级外带泄漏诊断，通过测量选择性扫描状态轨迹中的外带能量和扫描后Mamba读取中的外带能量，将隐藏状态存储与读取表达分开。在五个公开的MRI重建基准上进行的实验表明，SO-Mamba在具有竞争性计算效率的CNN、Transformer和Mamba基线中表现一致提升。
英文摘要：
Accelerated MRI reconstruction requires recovering missing details while preserving anatomically coherent structures across large spatial regions. State-space models such as Mamba provide efficient long-range modeling, making them attractive learned regularizers for unrolled reconstruction. However, in a data-consistency-coupled unrolled solver, different stages operate on different reconstruction iterates, where the resident carrier should preserve coherent reconstruction content across stages while stage-dependent non-resident evidence is tied to the current update. Treating these roles uniformly can place persistent resident-carrier evidence and update-dependent non-resident evidence into the same recurrent content route. We therefore propose SO-Mamba, a state-ownership Mamba regularizer that assigns reconstruction evidence within each Mamba stage to recurrent residency, state-interface access, and non-state output correction. SO-Mamba implements this ownership rule with a State-Ownership Router (SOR), which constructs a resident carrier for recurrent content and routes non-resident evidence to affine modulation of the B/C state interfaces and an output correction outlet. The resident carrier supplies the Mamba content route, while the non-resident evidence stream adapts the state interfaces and contributes through the output outlet without entering the recurrent content route. We further introduce a two-level outer-band leakage diagnostic that separates hidden-state storage from readout expression by measuring outer-band energy in the selective-scan state trajectory and the post-scan Mamba readout. Experiments on five public MRI reconstruction benchmarks spanning diverse anatomies, sampling patterns, and coil configurations show that SO-Mamba consistently improves over CNN-, Transformer-, and Mamba-based baselines with competitive computational efficiency.
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