Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA.
Magn Reson Imaging. 2011 Feb;29(2):222-9. doi: 10.1016/j.mri.2010.08.017. Epub 2010 Dec 8.
We introduce a novel noniterative algorithm for the fast and accurate reconstruction of nonuniformly sampled MRI data. The proposed scheme derives the reconstructed image as the nonuniform inverse Fourier transform of a compensated dataset. We derive each sample in the compensated dataset as a weighted linear combination of a few measured k-space samples. The specific k-space samples and the weights involved in the linear combination are derived such that the reconstruction error is minimized. The computational complexity of the proposed scheme is comparable to that of gridding. At the same time, it provides significantly improved accuracy and is considerably more robust to noise and undersampling. The advantages of the proposed scheme makes it ideally suited for the fast reconstruction of large multidimensional datasets, which routinely arise in applications such as f-MRI and MR spectroscopy. The comparisons with state-of-the-art algorithms on numerical phantoms and MRI data clearly demonstrate the performance improvement.
我们介绍了一种新颖的非迭代算法，用于快速准确地重建非均匀采样的 MRI 数据。所提出的方案将重建图像作为补偿数据集的非均匀逆傅里叶变换。我们将补偿数据集中的每个样本推导为几个测量的 k 空间样本的加权线性组合。涉及的特定 k 空间样本和权重是这样推导的，使得重建误差最小化。所提出的方案的计算复杂度与网格化相当。同时，它提供了显著提高的准确性，并且对噪声和欠采样更加稳健。所提出的方案的优势使其非常适合快速重建大型多维数据集，这些数据集在 f-MRI 和磁共振波谱等应用中经常出现。与数值体模和 MRI 数据上的最新算法的比较清楚地表明了性能的提高。
