IEEE Trans Med Imaging. 2018 Nov;37(11):2390-2402. doi: 10.1109/TMI.2018.2822053. Epub 2018 Apr 2.
Structured low-rank matrix models have previously been introduced to enable calibrationless MR image reconstruction from sub-Nyquist data, and such ideas have recently been extended to enable navigator-free echo-planar imaging (EPI) ghost correction. This paper presents a novel theoretical analysis which shows that, because of uniform subsampling, the structured low-rank matrix optimization problems for EPI data will always have either undesirable or non-unique solutions in the absence of additional constraints. This theory leads us to recommend and investigate problem formulations for navigator-free EPI that incorporate side information from either image-domain or k-space domain parallel imaging methods. The importance of using nonconvex low-rank matrix regularization is also identified. We demonstrate using phantom and in vivo data that the proposed methods are able to eliminate ghost artifacts for several navigator-free EPI acquisition schemes, obtaining better performance in comparison with the state-of-the-art methods across a range of different scenarios. Results are shown for both single-channel acquisition and highly accelerated multi-channel acquisition.
已引入结构化低秩矩阵模型，以便能够从亚奈奎斯特数据中进行无定标磁共振图像重建，最近此类思想已扩展到能够实现无导航器的回波平面成像（EPI）鬼影校正。本文提出了一种新的理论分析，表明由于均匀欠采样，在没有附加约束的情况下，EPI 数据的结构化低秩矩阵优化问题总是会产生不理想或非唯一的解。该理论促使我们推荐并研究了无导航器 EPI 的问题公式，这些公式结合了来自图像域或 k 空间域并行成像方法的辅助信息。还确定了使用非凸低秩矩阵正则化的重要性。我们使用体模和体内数据证明，所提出的方法能够消除几种无导航器 EPI 采集方案中的鬼影伪影，与各种不同情况下的最新方法相比，性能得到了改善。结果同时显示了单通道采集和高度加速的多通道采集。
