CD-DPE: Dual-Prompt Expert Network based on Convolutional Dictionary Feature Decoupling for Multi-Contrast MRI Super-Resolution
This work addresses the challenge of effectively utilizing reference images with different contrasts for MRI super-resolution, which is vital for early diagnosis and clinical decision-making, but it appears incremental as it builds on existing feature decoupling and fusion techniques.
The paper tackles multi-contrast MRI super-resolution by proposing CD-DPE, a dual-prompt expert network with convolutional dictionary feature decoupling, which outperforms state-of-the-art methods in reconstructing fine details and shows strong generalization on unseen datasets.
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.