Department of Electrical Engineering, Stanford University, Stanford, California, USA.
Magn Reson Med. 2011 Nov;66(5):1241-53. doi: 10.1002/mrm.22899. Epub 2011 May 20.
A novel iterative k-space data-driven technique, namely parallel reconstruction using null operations (PRUNO), is presented for parallel imaging reconstruction. In PRUNO, both data calibration and image reconstruction are formulated into linear algebra problems based on a generalized system model. An optimal data calibration strategy is demonstrated by using singular value decomposition, and an iterative conjugate-gradient approach is proposed to efficiently solve missing k-space samples during reconstruction. With its generalized formulation and precise mathematical model, PRUNO reconstruction yields good accuracy, flexibility, and stability. Both computer simulation and in vivo studies have shown that PRUNO produces much better reconstruction quality than generalized autocalibrating partially parallel acquisition (GRAPPA), especially under high accelerating rates. With the aid of PRUNO reconstruction, ultra-high accelerating parallel imaging can be performed with decent image quality. For example, we have done successful PRUNO reconstruction at a reduction factor of 6 (effective factor of 4.44) with eight coils and only a few autocalibration signal lines.
提出了一种新颖的迭代 k 空间数据驱动技术，即利用空操作的并行重建（PRUNO），用于并行成像重建。在 PRUNO 中，基于广义系统模型，将数据校准和图像重建都公式化为线性代数问题。通过奇异值分解展示了一种最优的数据校准策略，并提出了一种迭代共轭梯度方法，用于在重建过程中有效求解缺失的 k 空间样本。PRUNO 重建具有广义的公式和精确的数学模型，因此具有良好的准确性、灵活性和稳定性。计算机模拟和体内研究都表明，PRUNO 产生的重建质量比广义自校准部分并行采集（GRAPPA）要好得多，尤其是在高加速率下。借助 PRUNO 重建，可以实现具有良好图像质量的超高加速并行成像。例如，我们已经成功地在 6 倍降采样因子（有效因子为 4.44）下使用 8 个线圈和仅几条自校准信号线进行了 PRUNO 重建。
