Liao Congyu, Chen Ying, Cao Xiaozhi, Chen Song, He Hongjian, Mani Merry, Jacob Mathews, Magnotta Vincent, Zhong Jianhui
Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China.
Department of Psychiatry, University of Iowa, Iowa City, Iowa, USA.
Magn Reson Med. 2017 Mar;77(3):1359-1366. doi: 10.1002/mrm.26199. Epub 2016 Mar 10.
To propose a novel reconstruction method using parallel imaging with low rank constraint to accelerate high resolution multishot spiral diffusion imaging.
The undersampled high resolution diffusion data were reconstructed based on a low rank (LR) constraint using similarities between the data of different interleaves from a multishot spiral acquisition. The self-navigated phase compensation using the low resolution phase data in the center of k-space was applied to correct shot-to-shot phase variations induced by motion artifacts. The low rank reconstruction was combined with sensitivity encoding (SENSE) for further acceleration. The efficiency of the proposed joint reconstruction framework, dubbed LR-SENSE, was evaluated through error quantifications and compared with ℓ1 regularized compressed sensing method and conventional iterative SENSE method using the same datasets.
It was shown that with a same acceleration factor, the proposed LR-SENSE method had the smallest normalized sum-of-squares errors among all the compared methods in all diffusion weighted images and DTI-derived index maps, when evaluated with different acceleration factors (R = 2, 3, 4) and for all the acquired diffusion directions.
Robust high resolution diffusion weighted image can be efficiently reconstructed from highly undersampled multishot spiral data with the proposed LR-SENSE method. Magn Reson Med 77:1359-1366, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
提出一种利用并行成像和低秩约束的新型重建方法，以加速高分辨率多次激发螺旋扩散成像。
基于低秩（LR）约束，利用多次激发螺旋采集不同交错数据之间的相似性，对欠采样的高分辨率扩散数据进行重建。应用利用k空间中心低分辨率相位数据的自导航相位补偿来校正由运动伪影引起的逐次激发相位变化。低秩重建与灵敏度编码（SENSE）相结合以进一步加速。通过误差量化评估了所提出的联合重建框架（称为LR-SENSE）的效率，并与使用相同数据集的ℓ1正则化压缩感知方法和传统迭代SENSE方法进行了比较。
结果表明，在不同加速因子（R = 2、3、4）下，对于所有采集的扩散方向，在所有扩散加权图像和DTI衍生指数图中，使用相同加速因子时，所提出的LR-SENSE方法在所有比较方法中具有最小的归一化平方和误差。
使用所提出的LR-SENSE方法可以从高度欠采样的多次激发螺旋数据中高效地重建出稳健的高分辨率扩散加权图像。《磁共振成像杂志》77:1359 - 1366, 2017。© 2016国际磁共振医学学会。
