κ-space inherited parallel acquisition (KIPA):: application on dynamic magnetic resonance imaging thermometry
κ-space inherited parallel acquisition (KIPA):: application on dynamic magnetic resonance imaging thermometry
复制标题
DOI：
10.1016/j.mri.2006.03.001
复制
发表时间：
2006-09-01
影响因子：
2.5
通讯作者：
Parker, Dennis L.
中科院分区：
文献类型：
--
作者：
Guo, Jun-Yu;Kholmovski, Eugene G.;Parker, Dennis L.
提出了一种新的动态并行图像采集与重建方法。这种方法称为k空间继承并行捕获(KIPA)，与广义自校准部分并行捕获(GRAPPA)重建方法相比，局部化重建系数可获得更高的压缩因子、更低的噪声和伪影水平。在KIPA中，重建一系列图像需要第一帧的完整k空间和后续帧的部分k空间。使用为来自第一帧数据集的k空间的不同段计算的重构系数来估计在其他帧的相应k空间段中丢失的k空间线。Kipa重构系数的局部确定是根据k空间数据的局部信噪比特性调整重构系数的关键。该算法适用于任意k空间采样轨迹的动态成像。用KIPA方法对磁共振测温进行了模拟，并对4和6的人体进行了动态成像研究，证明了该方法的可行性，并且与GRAPPA的动态成像相比，图像质量有了明显的改善。(C)2006 Elsevier Inc.保留所有权利。
In this study, a novel method for dynamic parallel image acquisition and reconstruction is presented. In this method, called k-space inherited parallel acquisition (KIPA), localized reconstruction coefficients are used to achieve higher reduction factors, and lower noise and artifact levels compared to that of generalized autocalibrating partially parallel acquisition (GRAPPA) reconstruction. In KIPA, the full k-space for the first frame and the partial k-space for later frames are required to reconstruct a whole series of images. Reconstruction coefficients calculated for different segments of k-space from the first frame data set are used to estimate missing k-space lines in corresponding k-space segments of other frames. The local determination of KIPA reconstruction coefficients is essential to adjusting them according to the local signal-to-noise ratio characteristics of k-space data. The proposed algorithm is applicable to dynamic imaging with arbitrary k-space sampling trajectories. Simulations of magnetic resonance thermometry using the KIPA method with a reduction factor of 6 and using dynamic imaging studies of human subjects with reduction factors of 4 and 6 have been performed to prove the feasibility of our method and to show apparent improvement in image quality in comparison with GRAPPA for dynamic imaging. (c) 2006 Elsevier Inc. All rights reserved.