Abstract: Magnetic resonance imaging is an important tool for clinical auxiliary diagnosis and lesion detection. Currently, most MRI reconstruction methods are based on global feature modeling, utilizing transformers to achieve high-quality reconstruction. However, these methods often perform dense feature dependency calculations in the spatial domain, which may introduce redundant information and noise from irrelevant areas. Additionally, existing methods require separate training of models for different sampling patterns, resulting in inefficiency and limited generalization capabilities. To address these issues, this paper proposes the Dual-domain Adaptive Transformer Prompt Network (DATP-Net), a unified reconstruction framework that efficiently models feature relationships and reconstructs images from various sampling patterns simultaneously. The proposed network includes several core designs: (1) A deep feature convolution mixer that performs convolution operations in both spatial and frequency domains to enhance the representation of deep features; (2) An adaptive mixing transformer that combines adaptive self-attention and a fine-grained feedforward network, using dual-branch self-attention computation and fine feature elimination to enhance potentially useful feature relationships; (3) A degradation prompt module that injects learnable prior degradation information flow at the reconstruction end to guide feature reconstruction, enabling the network to integrate MR image reconstruction from multiple sampling patterns and enhance the model's generalization ability. Extensive experiments conducted on public IXI and fastMRI datasets demonstrate that the proposed method significantly outperforms state-of-the-art methods with lower computational costs. At a 4x random sampling rate, the model achieves an average PSNR of 39.82 and an SSIM exceeding 0.96, successfully reconstructing images with high clarity and detail restoration.

摘要： 磁共振成像是临床辅助诊断、病变检测的重要手段。当前大多数磁共振成像重建方法主要基于特征全局建模，利用变换器实现高质量重建。然而这些方法大多在空间域中进行密集的特征依赖关系计算，这可能导致引入冗余信息和来自无关区域的噪声。此外，现有方法需要为不同的采样模式单独训练模型，从而导致效率低下和有限的泛化能力。为了解决这些问题，本文提出了双域自适应变换器提示网络DATP-Net，这是一个统一的重建框架，能够高效地建模特征关系，并同时从各种采样模式中重建图像。该网络包括几个核心设计：(1)深度特征卷积混合器，它在空间和频率域中执行卷积操作，从而改善深度特征的表示；(2)自适应混合变换器，该变换器结合了自适应自注意力和精细前馈网络，通过双分支自注意力计算和细化特征消除冗余特征，增强潜在有用的特征关系；(3)退化提示模块，该模块在重建端注入可学习的先验退化信息流，以引导特征重建，使网络能够整合来自多种采样模式的MRI图像重建，并增强模型的泛化能力。在公开的IXI和fastMRI数据集上进行的广泛实验表明，提出的方法在更低的计算成本下显著优于最先进的方法。在4倍随机采样下，模型平均PSNR达到39.82且SSIM 超过0.96，能够重建高清晰度和细节还原的图像。