面向磁共振图像重建的k空间降采样优化

成像速度是关系磁共振临床应用效能的重要因素,在k空间中降采样,再配合图像重建,可有效加快成像速度.因此,文中考虑降采样方式对磁共振图像重建质量的影响,在训练深度学习网络进行磁共振图像重建的情况下,提出联合优化k空间降采样方式与重建模型的方法.从k空间全采样入手,逐步删除次要的相位编码,直到针对相位编码的采样满足稀疏性要求为止.同时,采样方式的优化是和深度学习图像重建模型参数优化交替进行,即赋予每个相位编码一个权重,通过权重大小确定相位编码的重要性,在优化重建网络参数的同时,完成对k空间降采样方式的优化.实验表明文中方法可提升磁共振图像重建质量.

Optimizing k-Space Subsampling Pattern toward MRI Reconstruction

Imaging velocity is a major factor affecting clinical applications of magnetic resonance(MR) imaging. And an effective solution of reducing scanning time is to under-sample in k-space and reconstruct the image from under-sampled MR signals. In this paper, the impact of under-sampling pattern on reconstruction quality is analyzed and a joint optimization strategy is proposed to update the under-sampling pattern with image reconstruction model in the context of deep-learning. To optimize the non-continuous under-sampling pattern, it is firstly initialized with full-sampling pattern. Then, relatively less important phase-encodings are gradually pruned until the sparsity requirement in k-space is satisfied. And the optimization of k-space under-sampling pattern is conducted alternatively with that of the reconstruction model. Moreover, the relative importance is estimated with the weight by assigning weight to each phase-coding. Experiments demonstrate that the proposed method improves the quality of the reconstructed MR image compared with the proposed method.