State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China.
Magn Reson Med. 2010 Jun;63(6):1691-7. doi: 10.1002/mrm.22336.
An efficient iterative gridding reconstruction method with correction of off-resonance artifacts was developed, which is especially tailored for multiple-shot non-Cartesian imaging. The novelty of the method lies in that the transformation matrix for gridding (T) was constructed as the convolution of two sparse matrices, among which the former is determined by the sampling interval and the spatial distribution of the off-resonance frequencies and the latter by the sampling trajectory and the target grid in the Cartesian space. The resulting T matrix is also sparse and can be solved efficiently with the iterative conjugate gradient algorithm. It was shown that, with the proposed method, the reconstruction speed in multiple-shot non-Cartesian imaging can be improved significantly while retaining high reconstruction fidelity. More important, the method proposed allows tradeoff between the accuracy and the computation time of reconstruction, making customization of the use of such a method in different applications possible. The performance of the proposed method was demonstrated by numerical simulation and multiple-shot spiral imaging on rat brain at 4.7 T.
开发了一种高效的迭代网格重建方法，具有校正离频伪影的功能，特别适用于多次非笛卡尔成像。该方法的新颖之处在于网格变换矩阵 (T) 构建为两个稀疏矩阵的卷积，其中前一个矩阵由采样间隔和离频的空间分布决定，后一个矩阵由采样轨迹和笛卡尔空间中的目标网格决定。得到的 T 矩阵也是稀疏的，可以用迭代共轭梯度算法有效地求解。结果表明，在所提出的方法中，在保持高重建保真度的同时，可以显著提高多次非笛卡尔成像的重建速度。更重要的是，该方法允许在重建的准确性和计算时间之间进行权衡，从而可以根据不同的应用定制该方法的使用。该方法的性能通过在 4.7T 大鼠脑的数值模拟和多次螺旋成像进行了验证。
