Samsonov Alexey A
Department of Radiology, University of Wisconsin-Madison, Clinical Science Center, Madison, Wisconsin 53792, USA.
Magn Reson Med. 2008 Jan;59(1):156-64. doi: 10.1002/mrm.21466.
Parallel MRI reconstruction in k-space has several advantages, such as tolerance to calibration data errors and efficient non-Cartesian data processing. These benefits largely accrue from the approximation that a given unsampled k-space datum can be synthesized from only a few local samples. In this study, several aspects of parallel MRI reconstruction in k-space are studied: the design of optimized reconstruction kernels, the effect of regularization on image error, and the accuracy of different k-space-based parallel MRI methods. Reconstruction of parallel MRI data in k-space is posed as the problem of approximating the pseudoinverse with a sparse matrix. The error of the approximation is used as an optimization criterion to find reconstruction kernels optimized for the given coil setup. An efficient algorithm for automatic selection of reconstruction kernels is described. Additionally, a total error metric is introduced for validation of the reconstruction kernel and choice of regularization parameters. The new methods yield reduced reconstruction and noise errors in both simulated and real data studies when compared with existing methods. The new methods may be useful for reduction of image errors, faster data processing, and validation of parallel MRI reconstruction design for a given coil system and k-space trajectory.
k空间中的并行磁共振成像重建具有若干优势，例如对校准数据误差的耐受性以及高效的非笛卡尔数据处理。这些优势主要源于这样一种近似，即给定的未采样k空间数据点可以仅从少数局部样本中合成。在本研究中，对k空间中并行磁共振成像重建的几个方面进行了研究：优化重建核的设计、正则化对图像误差的影响以及不同基于k空间的并行磁共振成像方法的准确性。k空间中并行磁共振成像数据的重建被视为用稀疏矩阵逼近伪逆的问题。逼近误差被用作优化标准，以找到针对给定线圈设置优化的重建核。描述了一种自动选择重建核的高效算法。此外，还引入了一种总误差度量，用于验证重建核和选择正则化参数。与现有方法相比，新方法在模拟和实际数据研究中均降低了重建误差和噪声误差。新方法可能有助于减少图像误差、加快数据处理速度以及验证给定线圈系统和k空间轨迹的并行磁共振成像重建设计。
