Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.
Magn Reson Imaging. 2012 Sep;30(7):964-77. doi: 10.1016/j.mri.2012.02.019. Epub 2012 Apr 13.
Compressed sensing has shown great potential in reducing data acquisition time in magnetic resonance imaging (MRI). In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a preconstructed basis or dictionary. In this paper, patch-based directional wavelets are proposed to reconstruct images from undersampled k-space data. A parameter of patch-based directional wavelets, indicating the geometric direction of each patch, is trained from the reconstructed image using conventional compressed sensing MRI methods and incorporated into the sparsifying transform to provide the sparse representation for the image to be reconstructed. A reconstruction formulation is proposed and solved via an efficient alternating direction algorithm. Simulation results on phantom and in vivo data indicate that the proposed method outperforms conventional compressed sensing MRI methods in preserving the edges and suppressing the noise. Besides, the proposed method is not sensitive to the initial image when training directions.
压缩感知在磁共振成像（MRI）中减少数据采集时间方面显示出巨大的潜力。在传统的压缩感知 MRI 方法中，通过对预先构建的基或字典施加其稀疏表示来重建图像。在本文中，提出了基于块的方向小波来从欠采样的 k 空间数据中重建图像。基于块的方向小波的一个参数，指示每个块的几何方向，是从使用传统的压缩感知 MRI 方法重建的图像中训练得到的，并将其合并到稀疏变换中，为要重建的图像提供稀疏表示。提出了一种重建公式，并通过有效的交替方向算法进行求解。对体模和体内数据的仿真结果表明，与传统的压缩感知 MRI 方法相比，该方法在保持边缘和抑制噪声方面表现更好。此外，在训练方向时，该方法对初始图像不敏感。
