论文标题
使用联合张量核标准和casorati矩阵核定常正常化的动态心脏MRI重建
Dynamic Cardiac MRI Reconstruction Using Combined Tensor Nuclear Norm and Casorati Matrix Nuclear Norm Regularizations
论文作者
论文摘要
低量张量模型已应用于加速动态磁共振成像（DMRI）。最近，已经提出了一种基于T-SVD的新张量核标准，并应用于张量完成。受张量核定常（TNN）和Casorati矩阵核标准（MNN）的不同特性的启发，我们引入了一个合并的TNN和Casorati MNN正规化框架以重建DMRI，我们称其为TMNN。所提出的方法同时利用了动态MR数据的空间结构和时间相关性。优化问题可以通过乘数的交替方向方法有效地解决。为了进一步提高计算效率，我们在笛卡尔采样方案下开发了一种快速算法。基于心脏CINE MRI和灌注MRI数据的数值实验表明，传统的Casorati核定常正则化方法的性能提高。
Low-rank tensor models have been applied in accelerating dynamic magnetic resonance imaging (dMRI). Recently, a new tensor nuclear norm based on t-SVD has been proposed and applied to tensor completion. Inspired by the different properties of the tensor nuclear norm (TNN) and the Casorati matrix nuclear norm (MNN), we introduce a combined TNN and Casorati MNN regularizations framework to reconstruct dMRI, which we term as TMNN. The proposed method simultaneously exploits the spatial structure and the temporal correlation of the dynamic MR data. The optimization problem can be efficiently solved by the alternating direction method of multipliers (ADMM). In order to further improve the computational efficiency, we develop a fast algorithm under the Cartesian sampling scenario. Numerical experiments based on cardiac cine MRI and perfusion MRI data demonstrate the performance improvement over the traditional Casorati nuclear norm regularization method.
