用Mamba模型分离多对比MRI中的模态信息，提升重建质量。
Mamba-Based Modality Disentanglement Network for Multi-Contrast MRI Reconstruction
- 基于Mamba的双域网络，分离不同模态特征。
- 在多种数据集上优于现有方法，显著减少伪影。
- 适合需要高精度多对比MRI重建的研究者。
磁共振成像（MRI）是现代临床诊断的核心，提供无电离辐射的软组织对比。然而，扫描时间过长仍是影响患者效率与舒适度的主要障碍。现有加速MRI技术常面临两大挑战：(1) 未能有效利用K空间先验信息，导致零填充输入持续存在混叠伪影；(2) 多对比融合策略引入无关信息，污染目标重建质量。为此，我们提出MambaMDN，一种用于多对比MRI重建的双域框架。首先，利用全采样参考K空间数据完成欠采样目标数据，生成结构对齐但模态混合的输入。随后，设计基于Mamba的模态解耦网络，从混合表示中提取并去除参考特异性特征。此外，引入迭代精炼机制，通过反复特征净化逐步提升重建精度。大量实验表明，MambaMDN能显著超越现有方法。
原文摘要 · Abstract (English)
Magnetic resonance imaging (MRI) is a cornerstone of modern clinical diagnosis, offering unparalleled soft-tissue contrast without ionizing radiation. However, prolonged scan times remain a major barrier to patient throughput and comfort. Existing accelerated MRI techniques often struggle with two key challenges: (1) failure to effectively utilize inherent K-space prior information, leading to persistent aliasing artifacts from zero-filled inputs; and (2) contamination of target reconstruction quality by irrelevant information when employing multi-contrast fusion strategies. To overcome these challenges, we present MambaMDN, a dual-domain framework for multi-contrast MRI reconstruction. Our approach first employs fully-sampled reference K-space data to complete the undersampled target data, generating structurally aligned but modality-mixed inputs. Subsequently, we develop a Mamba-based modality disentanglement network to extract and remove reference-specific features from the mixed representation. Furthermore, we introduce an iterative refinement mechanism to progressively enhance reconstruction accuracy through repeated feature purification. Extensive experiments demonstrate that MambaMDN can significantly outperform existing multi-contrast reconstruction methods.
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