Multi-contrast magnetic resonance imaging (MRI) super-resolution intends to reconstruct high-resolution (HR) images from low-resolution (LR) scans by leveraging structural information present in HR reference images acquired with different contrasts. This technique enhances anatomical detail and soft tissue differentiation, which is vital for early diagnosis and clinical decision-making. However, inherent contrasts disparities between modalities pose fundamental challenges in effectively utilizing reference image textures to guide target image reconstruction, often resulting in suboptimal feature integration. To address this issue, we propose a dual-prompt expert network based on a convolutional dictionary feature decoupling (CD-DPE) strategy for multi-contrast MRI super-resolution. Specifically, we introduce an iterative convolutional dictionary feature decoupling module (CD-FDM) to separate features into cross-contrast and intra-contrast components, thereby reducing redundancy and interference. To fully integrate these features, a novel dual-prompt feature fusion expert module (DP-FFEM) is proposed. This module uses a frequency prompt to guide the selection of relevant reference features for incorporation into the target image, while an adaptive routing prompt determines the optimal method for fusing reference and target features to enhance reconstruction quality. Extensive experiments on public multi-contrast MRI datasets demonstrate that CD-DPE outperforms state-of-the-art methods in reconstructing fine details. Additionally, experiments on unseen datasets demonstrated that CD-DPE exhibits strong generalization capabilities.

翻译：多对比度磁共振成像（MRI）超分辨率重建旨在通过利用不同对比度下获取的高分辨率（HR）参考图像中的结构信息，从低分辨率（LR）扫描中重建出高分辨率图像。该技术增强了解剖细节和软组织区分度，对早期诊断和临床决策至关重要。然而，不同模态之间固有的对比度差异对有效利用参考图像纹理来指导目标图像重建构成了根本性挑战，常导致特征整合效果欠佳。为解决此问题，我们提出了一种基于卷积字典特征解耦（CD-DPE）策略的双提示专家网络，用于多对比度MRI超分辨率重建。具体而言，我们引入了一个迭代卷积字典特征解耦模块（CD-FDM），将特征分离为跨对比度和对比度内分量，从而减少冗余和干扰。为充分整合这些特征，提出了一种新颖的双提示特征融合专家模块（DP-FFEM）。该模块使用频率提示来指导选择相关的参考特征以融入目标图像，同时通过自适应路由提示确定融合参考与目标特征的最佳方法，以提升重建质量。在公开多对比度MRI数据集上的大量实验表明，CD-DPE在重建精细细节方面优于现有先进方法。此外，在未见数据集上的实验证明，CD-DPE展现出强大的泛化能力。