Abstract：
The reconstruction of dynamic magnetic resonance images from incomplete k-space data has sparked significant research interest due to its potential to reduce scan time. However, traditional iterative optimization algorithms fail to faithfully reconstruct images at higher acceleration factors and incur long reconstruction time. Furthermore, end-to-end deep learning-based reconstruction algorithms suffer from large model parameters and lack robustness in the reconstruction results. Recently, unrolled deep learning models, have shown immense potential in algorithm stability and applicability flexibility. In this paper, we propose an unrolled deep learning network based on a second-order Half-Quadratic Splitting(HQS) algorithm, where the forward propagation process of this framework strictly follows the computational flow of the HQS algorithm. In particular, we propose a degradation-sense module by associating random sampling patterns with intermediate variables to guide the iterative process. We introduce the Information Fusion Transformer(IFT) to extract both local and non-local prior information from image sequences, thereby removing aliasing artifacts resulting from random undersampling. Finally, we impose low-rank constraints within the HQS algorithm to further enhance the reconstruction results. The experiments demonstrate that each component module of our proposed model contributes to the improvement of the reconstruction task. Our proposed method achieves comparably satisfying performance to the state-of-the-art methods and it exhibits excellent generalization capabilities across different sampling masks. At the low acceleration factor, there is a 0.7% enhancement in the PSNR. Furthermore, when the acceleration factor reached 8 and 12, the PSNR achieves an improvement of 3.4% and 5.8% respectively.
摘要：
从不完整的k空间数据重建动态磁共振图像由于其减少扫描时间的潜力而引起了重大的研究兴趣。然而,传统的迭代优化算法无法在较高的加速因子下忠实地重建图像，并且重建时间长。此外，基于端到端深度学习的重建算法存在模型参数大、重建结果缺乏鲁棒性等问题。最近,展开的深度学习模型，在算法稳定性和适用性灵活性方面显示出巨大的潜力。在本文中,我们提出了一个基于二阶半二次分裂（HQS）算法的展开深度学习网络，其中，该框架的前向传播过程严格遵循HQS算法的计算流程。特别是,我们提出了一个退化感知模块，通过将随机采样模式与中间变量相关联来指导迭代过程。我们引入了信息融合转换器（IFT）来从图像序列中提取局部和非局部先验信息，从而消除随机欠采样产生的混叠伪影。最后,我们在HQS算法中施加低秩约束以进一步增强重建结果。实验表明，我们提出的模型的每个组件模块有助于改善重建任务。我们提出的方法与最先进的方法相比，具有令人满意的性能，并且在不同的采样掩码中具有出色的泛化能力。在低加速因子下，PSNR提高了0.7%。此外，当加速因子达到8和12时，PSNR分别提高了3.4%和5.8%。
