Data Convolution and Combination Operation (COCOA) for Motion Ghost Artifacts Reduction
Data Convolution and Combination Operation (COCOA) for Motion Ghost Artifacts Reduction
复制标题
DOI：
10.1002/mrm.22358
复制
发表时间：
2010-07-01
影响因子：
3.3
通讯作者：
Reykowski, Arne
中科院分区：
文献类型：
--
作者：
Huang, Feng;Lin, Wei;Reykowski, Arne
本文介绍了一种新的方法，即数据卷积和组合运算，用于减少数据采集过程中由于运动或流动而产生的重影伪影。由于来自不同线圈元件的相邻k空间数据点具有强相关性，因此可以通过针对每个线圈的卷积来生成具有分散的运动伪影的新的“合成”k空间。可以使用所采集的k空间数据自校准对应的卷积核。可以检查合成数据集和采集数据集的一致性，以识别运动损坏的k空间区域。随后，可以适当地组合这两个数据集以产生k空间数据集，该k空间数据集示出了降低水平的运动引起的误差。如果采集到的k空间中存在孤立误差，可以通过数据卷积和合并运算完全消除。如果所采集的k空间数据包含广泛的误差，则卷积的应用还显著降低了总体误差。模拟和体内数据的结果表明，这种自校准方法鲁棒地减少了由于吞咽，呼吸或血流的鬼伪影，对图像信噪比的影响最小。Magn Reson Med 64：157-166，2010. (C)2010 Wiley-Liss，Inc.
A novel method, data convolution and combination operation, is introduced for the reduction of ghost artifacts due to motion or flow during data acquisition. Since neighboring k-space data points from different coil elements have strong correlations, a new "synthetic' k-space with dispersed motion artifacts can be generated through convolution for each coil. The corresponding convolution kernel can be self-calibrated using the acquired k-space data. The synthetic and the acquired data sets can be checked for consistency to identify k-space areas that are motion corrupted. Subsequently, these two data sets can be combined appropriately to produce a k-space data set showing a reduced level of motion induced error. If the acquired k-space contains isolated error, the error can be completely eliminated through data convolution and combination operation. If the acquired k-space data contain widespread errors, the application of the convolution also significantly reduces the overall error. Results with simulated and in vivo data demonstrate that this self-calibrated method robustly reduces ghost artifacts due to swallowing, breathing, or blood flow, with a minimum impact on the image signal-to-noise ratio. Magn Reson Med 64:157-166, 2010. (C) 2010 Wiley-Liss, Inc.