An improved non-Cartesian partially parallel imaging by exploiting artificial sparsity
An improved non-Cartesian partially parallel imaging by exploiting artificial sparsity
复制标题
利用人工稀疏性改进的非笛卡尔部分并行成像
DOI：
10.1002/mrm.26360
复制
发表时间：
2017-07-01
影响因子：
3.3
通讯作者：
Huang, Feng
中科院分区：
文献类型：
--
作者：
Chen, Zhifeng;Xia, Ling;Huang, Feng
目的利用人工稀疏性提高非笛卡尔部分并行成像（PPI）的性能，本文以广义自动校准部分并行采集（GRAPPA）算子（GROWL）为例进行了说明。方法提出了一种系统的方案，人工生成稀疏图像的非笛卡尔轨迹。使用GROWL作为一个特定的非笛卡尔PPI方法，人工稀疏增强的GROWL（ARTS-GROWL）被用来证明所提出的计划的效率。ARTS-GROWL包括三个步骤：1）生成与具有较小支持度（即人工稀疏度）的图像相对应的合成k空间数据; 2）将GROWL应用于来自前一步骤的合成k空间数据;以及3）使用处理后的数据从重建中恢复最终图像。结果对于模拟和体内数据，实验表明，对于测试的加速因子，与传统的GROWL技术相比，所提出的ARTS-GROWL显著降低了重建误差。结论以ARTS-GROWL为例，实验结果表明，人工稀疏改善了非笛卡尔PPI的信噪比和归一化均方根误差。Magn Reson Med 78：271-279，2017年。(c)2016年国际医学磁共振学会
PurposeTo improve the performance of non-Cartesian partially parallel imaging (PPI) by exploiting artificial sparsity, the generalized autocalibrating partially parallel acquisitions (GRAPPA) operator for wider band lines (GROWL) is taken as a specific example for explanation.TheoryThis work is based on the GRAPPA-like PPI having an improved performance when the to-be-reconstructed image is sparse in the image domain.MethodsA systematic scheme is proposed to artificially generate the sparse image for non-Cartesian trajectory. Using GROWL as a specific non-Cartesian PPI method, artificial sparsity-enhanced GROWL (ARTS-GROWL) is used to demonstrate the efficiency of the proposed scheme. The ARTS-GROWL consists of three steps: 1) generating synthetic k-space data corresponding to an image with smaller support, that is, artificial sparsity; 2) applying GROWL to the synthetic k-space data from previous step; and 3) recovering the final image from the reconstruction with the processed data.ResultsFor simulation and in vivo data, the experiments demonstrate that the proposed ARTS-GROWL significantly reduces the reconstruction errors compared with the conventional GROWL technique for the tested acceleration factors.ConclusionTaking ARTS-GROWL, for instance, experimental results indicate that artificial sparsity improved the signal-to-noise ratio and normalized root-mean-square error of non-Cartesian PPI. Magn Reson Med 78:271-279, 2017. (c) 2016 International Society for Magnetic Resonance in Medicine