Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.
Magn Reson Imaging. 2013 Jan;31(1):75-85. doi: 10.1016/j.mri.2012.06.028. Epub 2012 Aug 15.
Compressed sensing (CS) and partially parallel imaging (PPI) enable fast magnetic resonance (MR) imaging by reducing the amount of k-space data required for reconstruction. Past attempts to combine these two have been limited by the incoherent sampling requirement of CS since PPI routines typically sample on a regular (coherent) grid. Here, we developed a new method, "CS+GRAPPA," to overcome this limitation. We decomposed sets of equidistant samples into multiple random subsets. Then, we reconstructed each subset using CS and averaged the results to get a final CS k-space reconstruction. We used both a standard CS and an edge- and joint-sparsity-guided CS reconstruction. We tested these intermediate results on both synthetic and real MR phantom data and performed a human observer experiment to determine the effectiveness of decomposition and to optimize the number of subsets. We then used these CS reconstructions to calibrate the generalized autocalibrating partially parallel acquisitions (GRAPPA) complex coil weights. In vivo parallel MR brain and heart data sets were used. An objective image quality evaluation metric, Case-PDM, was used to quantify image quality. Coherent aliasing and noise artifacts were significantly reduced using two decompositions. More decompositions further reduced coherent aliasing and noise artifacts but introduced blurring. However, the blurring was effectively minimized using our new edge- and joint-sparsity-guided CS using two decompositions. Numerical results on parallel data demonstrated that the combined method greatly improved image quality as compared to standard GRAPPA, on average halving Case-PDM scores across a range of sampling rates. The proposed technique allowed the same Case-PDM scores as standard GRAPPA using about half the number of samples. We conclude that the new method augments GRAPPA by combining it with CS, allowing CS to work even when the k-space sampling pattern is equidistant.
压缩感知（CS）和部分并行成像（PPI）通过减少重建所需的 k 空间数据量来实现快速磁共振（MR）成像。过去，由于 CS 对非相干采样的要求，将这两种方法结合起来的尝试受到限制，因为 PPI 例程通常在规则（相干）网格上采样。在这里，我们开发了一种新方法“CS+GRAPPA”来克服这一限制。我们将等距样本集分解为多个随机子集。然后，我们使用 CS 重建每个子集，并对结果进行平均以获得最终的 CS k 空间重建。我们使用了标准 CS 和边缘和联合稀疏度引导的 CS 重建。我们在合成和真实的 MR 体模数据上测试了这些中间结果，并进行了人类观察者实验，以确定分解的有效性并优化子集的数量。然后，我们使用这些 CS 重建来校准广义自校准部分并行采集（GRAPPA）复杂线圈权重。使用了体内并行 MR 脑和心脏数据集。使用客观图像质量评估指标 Case-PDM 来量化图像质量。使用两种分解可以显着减少相干伪影和噪声伪影。更多的分解进一步减少了相干伪影和噪声伪影，但引入了模糊。然而，使用我们新的边缘和联合稀疏度引导的 CS 进行两种分解可以有效地最小化模糊。并行数据的数值结果表明，与标准 GRAPPA 相比，组合方法大大提高了图像质量，平均在一系列采样率下将 Case-PDM 分数减半。该技术允许使用大约一半的样本数获得与标准 GRAPPA 相同的 Case-PDM 分数。我们得出的结论是，该新技术通过将 CS 与 GRAPPA 相结合来增强 GRAPPA，允许 CS 在 k 空间采样模式为等距时也能正常工作。
