Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.
School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.
Comput Math Methods Med. 2014;2014:134058. doi: 10.1155/2014/134058. Epub 2014 Mar 26.
A sparse-representation-based direct minimum L (p) -norm algorithm is proposed for a two-dimensional MRI phase unwrapping. First, the algorithm converts the weighted-L (p) -norm-minimization-based phase unwrapping problem into a linear system problem whose system (coefficient) matrix is a large, symmetric one. Then, the coefficient-matrix is represented in the sparse structure. Finally, standard direct solvers are employed to solve this linear system. Several wrapped phase datasets, including simulated and MR data, were used to evaluate this algorithm's performance. The results demonstrated that the proposed algorithm for unwrapping MRI phase data is reliable and robust.
提出了一种基于稀疏表示的直接最小 L(p)范数算法，用于二维 MRI 相位解缠。首先，该算法将基于加权 L(p)范数最小化的相位解缠问题转换为线性系统问题，其系统（系数）矩阵是一个大型对称矩阵。然后，用稀疏结构表示系数矩阵。最后，采用标准的直接求解器来求解这个线性系统。使用了几个包裹相位数据集，包括模拟和磁共振数据，来评估该算法的性能。结果表明，该算法用于解缠 MRI 相位数据是可靠和鲁棒的。
