Lin Fa-Hsuan, Wang Fu-Nien, Ahlfors Seppo P, Hämäläinen Matti S, Belliveau John W
Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.
Magn Reson Med. 2007 Oct;58(4):735-44. doi: 10.1002/mrm.21356.
Multiple receivers can be utilized to enhance the spatiotemporal resolution of MRI by employing the parallel imaging technique. Previously, we have reported the L-curve Tikhonov regularization technique to mitigate noise amplification resulting from the geometrical correlations between channels in a coil array. Nevertheless, one major disadvantage of regularized image reconstruction is lengthy computational time in regularization parameter estimation. At a fixed noise level, L-curve regularization parameter estimation was also found not to be robust across repetitive measurements, particularly for low signal-to-noise ratio (SNR) acquisitions. Here we report a computationally efficient and robust method to estimate the regularization parameter by partitioning the variance of the noise-whitened encoding matrix based on the estimated SNR of the aliased pixel set in parallel MRI data. The proposed Variance Partitioning Regularization (VPR) method can improve computational efficiency by 2-5-fold, depending on image matrix sizes and acceleration rates. Our anatomical and functional MRI results show that the VPR method can be applied to both static and dynamic MRI experiments to suppress noise amplification in parallel MRI reconstructions for improved image quality.
通过采用并行成像技术，可以利用多个接收器来提高MRI的时空分辨率。此前，我们报道了L曲线Tikhonov正则化技术，以减轻线圈阵列中通道间几何相关性导致的噪声放大。然而，正则化图像重建的一个主要缺点是正则化参数估计的计算时间较长。在固定噪声水平下，还发现L曲线正则化参数估计在重复测量中不够稳健，特别是对于低信噪比（SNR）采集。在此，我们报告一种计算高效且稳健的方法，通过基于并行MRI数据中混叠像素集的估计SNR对噪声白化编码矩阵的方差进行划分来估计正则化参数。所提出的方差划分正则化（VPR）方法可将计算效率提高2至5倍，具体取决于图像矩阵大小和加速率。我们的解剖学和功能MRI结果表明，VPR方法可应用于静态和动态MRI实验，以抑制并行MRI重建中的噪声放大，从而提高图像质量。
