SO-Mamba: State-Ownership Mamba for Unrolled MRI Reconstruction
Pengcheng Fang, Hongli Chen, Fangfang Tang, Feng Liu, Xiaohao Cai, Shanshan Shan

TL;DR
SO-Mamba introduces a novel state-ownership regularizer with a dedicated routing mechanism to improve unrolled MRI reconstruction, effectively preserving coherent structures across stages.
Contribution
It proposes the SO-Mamba regularizer and State-Ownership Router to distinguish recurrent and non-resident evidence, enhancing MRI reconstruction quality.
Findings
Consistently outperforms CNN-, Transformer-, and Mamba-based baselines.
Effective across diverse MRI benchmarks, anatomies, and sampling patterns.
Maintains competitive computational efficiency.
Abstract
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,…
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