Dynamic autocalibrated parallel imaging using temporal GRAPPA (TGRAPPA)
Dynamic autocalibrated parallel imaging using temporal GRAPPA (TGRAPPA)
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DOI：
10.1002/mrm.20430
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发表时间：
2005-04-01
影响因子：
3.3
通讯作者：
Jakob, PM
中科院分区：
文献类型：
--
作者：
Breuer, FA;Kellman, P;Jakob, PM
当前用于加速成像的并行成像技术需要一个完全编码的参考数据集，以估计重建所需的空间线圈灵敏度信息。在动态并行成像中，可以使用时间间隔的采集方案，这消除了单独获取其他参考数据的需求，因为可以合并来自直接相邻时间框架的信号以构建一组完全编码的全分辨率参考数据，以用于线圈的线圈校准。在这项工作中，我们证明了一个时间间隔的采样方案与自动校准的Grappa（称为Tgrappa）结合使用，使人们可以动态地轻松地更新Grappa算法的线圈权重，从而提高采集效率。该方法可能会按框架更新线圈灵敏度估计，从而跟踪数据采集过程中可能发生的相对线圈敏感性的变化。出版于2005 Wiley-Liss，Inc。
Current parallel imaging techniques for accelerated imaging require a fully encoded reference data set to estimate the spatial coil sensitivity information needed for reconstruction. In dynamic parallel imaging a time-interleaved acquisition scheme can be used, which eliminates the need for separately acquiring additional reference data, since the signal from directly adjacent time frames can be merged to build a set of fully encoded full-resolution reference data for coil calibration. In this work, we demonstrate that a time-interleaved sampling scheme, in combination with autocalibrated GRAPPA (referred to as TGRAPPA), allows one to easily update the coil weights for the GRAPPA algorithm dynamically, thereby improving the acquisition efficiency. This method may update coil sensitivity estimates frame by frame, thereby tracking changes in relative coil sensitivities that may occur during the data acquisition. Published 2005 Wiley-Liss, Inc.