Abstract
Global token mixing, implemented via self-attention or state-space sequence models, has become a popular model design choice for MRI restoration. However, MRI restoration tasks differ substantially in how their degradations vary over image and k-space domains, and in the degree to which global coupling is already imposed by physics-driven data consistency terms. In this work, we ask the question whether global token mixing is actually beneficial in each individual task across three representative settings: accelerated MRI reconstruction with explicit data consistency, MRI super-resolution with k-space center cropping, and denoising of clinical carotid MRI data with spatially heteroscedastic noise. To reduce confounding factors, we establish a controlled testbed comparing a minimal local gated CNN and its large-field variant, benchmarking them directly against state-of-the-art global models under aligned training and evaluation protocols. For accelerated MRI reconstruction, the minimal unrolled gated-CNN baseline is already highly competitive compared to recent token-mixing approaches in public reconstruction benchmarks, suggesting limited additional benefits when the forward model and data-consistency steps provide strong global constraints. For super-resolution, where low-frequency k-space data are largely preserved by the controlled low-pass degradation, local gated models remain competitive, and a lightweight large-field variant yields only modest improvements. In contrast, for denoising with pronounced spatially heteroscedastic noise, token-mixing models achieve the strongest overall performance, consistent with the need to estimate spatially varying reliability. In conclusion, our results demonstrate that the utility of global token mixing in MRI restoration is task-dependent, and it should be tailored to the underlying imaging physics and degradation structure.
Abstract (translated)
全球令牌混合（通过自注意力或状态空间序列模型实现）已成为MRI重建任务中流行的模型设计选择。然而，不同MRI重建任务在图像和k-空间领域中的退化变化方式以及物理驱动数据一致性项所带来的全局耦合程度上存在显著差异。在这项工作中，我们探讨了全球令牌混合是否真的对每一单独的任务都有益处，具体是在三个具有代表性的设置中：加速MRI重建（采用显式数据一致性）、基于k-空间中心裁剪的MRI超分辨率以及临床颈动脉MRI数据去噪（含空间异质噪声）。为了减少混淆因素的影响，我们建立了一个受控测试环境，在该环境中比较了最小局部门控CNN及其大范围变体，并直接将其与最先进的全局模型在一致的训练和评估协议下进行基准对比。 对于加速MRI重建任务，最小展开的门控-CNN基线已与最近的令牌混合方法相比在公共重建基准中具有高度竞争力，这表明当正向模型及数据一致性步骤提供了强大的全球约束时，额外采用令牌混合所带来的好处有限。对于超分辨率任务，在受控制低通滤波退化下大多数低频k-空间数据被保留的情况下，局部门控模型仍然保持竞争性，并且轻量级的大范围变体仅带来轻微的改进。相比之下，在存在显著空间异质噪声的去噪任务中，令牌混合模型实现了最强的整体性能，这与估计空间变化可靠性的需求一致。 综上所述，我们的研究结果表明在MRI重建中的全球令牌混合效用取决于具体任务，并且应根据底层成像物理和退化结构进行量身定制。
URL
https://arxiv.org/abs/2603.01449
