Liu Wentao, Zhao Xuna, Ma Yajun, Tang Xin, Gao Jia-Hong
Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.
Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Magn Reson Med. 2016 Jan;75(1):280-6. doi: 10.1002/mrm.25586. Epub 2015 Mar 5.
A novel k-space reconstruction method is proposed for generating diffusion-weighted imaging (DWI) using navigated interleaved multishot EPI (msEPI).
In interleaved msEPI, each shot of data acquired from one coil channel is a subset of the full k-space of that channel. All the k-space subsets of one channel can be treated as an undersampled dataset of a virtual multichannel data, which can be reconstructed by the GRAPPA algorithm after k-space realignment. The intershot phase variations are directly compensated using navigator echoes as the auto-calibrating data in GRAPPA reconstruction. In cases of multichannel msEPI data, all the virtual channels and actual channels can be integrated into a single GRAPPA reconstruction step. The proposed method is tested using both simulation and in-vivo data. The simulation results produced by the proposed method and a SENSE-based method are compared.
The simulated images generated by the proposed method exhibit less relative error compared with those generated by the SENSE method. Inconsistent shot-to-shot phase variation is naturally resolved by GRAPPA calibration without additional phase map processing. High-quality brain DWI with submillimeter resolution is obtained using our proposed reconstruction method.
A novel k-space msEPI reconstruction method has been developed for generating high-quality diffusion imaging.
提出一种新的k空间重建方法，用于使用导航交错多激发回波平面成像（msEPI）生成扩散加权成像（DWI）。
在交错msEPI中，从一个线圈通道采集的每一帧数据都是该通道完整k空间的一个子集。一个通道的所有k空间子集可被视为一个虚拟多通道数据的欠采样数据集，在k空间重排后可通过GRAPPA算法进行重建。在GRAPPA重建中，使用导航回波作为自动校准数据直接补偿帧间相位变化。对于多通道msEPI数据，所有虚拟通道和实际通道可整合到单个GRAPPA重建步骤中。使用模拟数据和体内数据对所提出的方法进行测试。比较了所提出的方法和基于敏感性编码（SENSE）的方法产生的模拟结果。
与SENSE方法生成的模拟图像相比，所提出的方法生成的模拟图像具有更小的相对误差。GRAPPA校准自然地解决了不一致的帧间相位变化，无需额外的相位图处理。使用我们提出的重建方法获得了具有亚毫米分辨率的高质量脑DWI。
已开发出一种新的k空间msEPI重建方法，用于生成高质量的扩散成像。
