Medical image reconstruction from undersampled acquisitions is an ill-posed problem that involves inversion of the imaging operator linking measurement and image domains. In recent years, physics-driven (PD) models have gained prominence in learning-based reconstruction given their enhanced balance between efficiency and performance. For reconstruction, PD models cascade data-consistency modules that enforce fidelity to acquired data based on the imaging operator, with network modules that process feature maps to alleviate image artifacts due to undersampling. Success in artifact suppression inevitably depends on the ability of the network modules to tease apart artifacts from underlying tissue structures, both of which can manifest contextual relations over broad spatial scales. Convolutional modules that excel at capturing local correlations are relatively insensitive to non-local context. While transformers promise elevated sensitivity to non-local context, practical implementations often suffer from a suboptimal trade-off between local and non-local sensitivity due to intrinsic model complexity. Here, we introduce a novel physics-driven autoregressive state space model (MambaRoll) for enhanced fidelity in medical image reconstruction. In each cascade of an unrolled architecture, MambaRoll employs an autoregressive framework based on physics-driven state space modules (PSSM), where PSSMs efficiently aggregate contextual features at a given spatial scale while maintaining fidelity to acquired data, and autoregressive prediction of next-scale feature maps from earlier spatial scales enhance capture of multi-scale contextual features. Demonstrations on accelerated MRI and sparse-view CT reconstructions indicate that MambaRoll outperforms state-of-the-art PD methods based on convolutional, transformer and conventional SSM modules.

翻译：医学图像重建从欠采样采集是一个不适定问题，涉及成像算子连接测量域与图像域的逆问题求解。近年来，物理驱动（PD）模型在基于学习的重建中日益突出，因其在效率与性能之间实现了更优的平衡。对于重建任务，PD模型将基于成像算子、强制与采集数据一致性的数据一致性模块，与处理特征图以减轻欠采样所致图像伪影的网络模块级联。伪影抑制的成功必然依赖于网络模块区分伪影与底层组织结构的能力，二者均可能在广阔的空间尺度上表现出上下文关联。擅长捕捉局部相关性的卷积模块对非局部上下文相对不敏感。尽管Transformer有望提升对非局部上下文的敏感性，但实际实现常因内在模型复杂性而在局部与非局部敏感性之间面临次优权衡。本文提出一种新颖的物理驱动自回归状态空间模型（MambaRoll），以提升医学图像重建的保真度。在展开架构的每一级联中，MambaRoll采用基于物理驱动状态空间模块（PSSM）的自回归框架，其中PSSM在给定空间尺度上高效聚合上下文特征，同时保持与采集数据的一致性，而基于先前空间尺度对下一尺度特征图的自回归预测则增强了对多尺度上下文特征的捕捉。在加速MRI与稀疏视图CT重建上的实验表明，MambaRoll在性能上超越了基于卷积、Transformer及传统SSM模块的先进PD方法。