Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization
Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization
复制标题
利用可分离和增强的低阶 Hankel 正则化加速 MRI 重建
DOI：
10.1109/tmi.2022.3164472
复制
发表时间：
2022
影响因子：
10.6
通讯作者：
Xiaobo Qu
中科院分区：
文献类型：
--
作者：
Xinlin Zhang;Hengfa Lu;Di Guo;Zongying Lai;Huihui Ye;Xi Peng;Bo Zhao;Xiaobo Qu
磁共振成像是临床诊断的重要工具，但其采集时间长。稀疏采样有效地节省了时间，但需要根据欠采样数据忠实地重建图像。在现有的重建方法中，结构化低秩方法具有对采样模式的鲁棒性和较低误差的优点。然而，结构化低秩方法使用二维或更高维度的k空间数据来构建巨大的块汉克尔矩阵，导致大量的时间和内存消耗。为了减小 Hankel 矩阵的大小，我们提出从 k 空间的行和列分别构造多个小 Hankel 矩阵，然后约束这些小矩阵的低秩。这种可分离模型可以显着减少计算时间，但忽略了行或列之间和行内存在的相关性，导致重建误差增加。为了在不明显增加计算量的情况下改善重建图像，我们进一步引入了k空间和虚拟线圈先验的自洽性。此外，所提出的可分离模型可以扩展到在参数维度上具有指数特征的其他成像场景。体内实验结果表明，所提出的方法能够以快速重建的方式实现最低的重建误差。所提出的方法仅需要最先进的 STDLR-SPIRiT 运行时间的 4% 来进行并行成像重建，并且在参数成像重建中实现了最快的计算速度。
Magnetic resonance imaging serves as an essential tool for clinical diagnosis, however, suffers from a long acquisition time. Sparse sampling effectively saves this time but images need to be faithfully reconstructed from undersampled data. Among the existing reconstruction methods, the structured low-rank methods have advantages in robustness to the sampling patterns and lower error. However, the structured low-rank methods use the 2D or higher dimension k-space data to build a huge block Hankel matrix, leading to considerable time and memory consumption. To reduce the size of the Hankel matrix, we proposed to separably construct multiple small Hankel matrices from rows and columns of the k-space and then constrain the low-rankness on these small matrices. This separable model can significantly reduce the computational time but ignores the correlation existed in inter- and intra-row or column, resulting in increased reconstruction error. To improve the reconstructed image without obviously increasing the computation, we further introduced the self-consistency of k-space and virtual coil prior. Besides, the proposed separable model can be extended into other imaging scenarios which hold exponential characteristics in the parameter dimension. Thein vivoexperimental results demonstrated that the proposed method permits the lowest reconstruction error with a fast reconstruction. The proposed approach requires only 4% of the state-of-the-art STDLR-SPIRiT runtime for parallel imaging reconstruction, and achieves the fastest computational speed in parameter imaging reconstruction.