Wright Katherine L, Lee Gregory R, Ehses Philipp, Griswold Mark A, Gulani Vikas, Seiberlich Nicole
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
J Magn Reson Imaging. 2014 Oct;40(4):864-74. doi: 10.1002/jmri.24439. Epub 2014 Jan 21.
To achieve high temporal and spatial resolution for contrast-enhanced time-resolved MR angiography exams (trMRAs), fast imaging techniques such as non-Cartesian parallel imaging must be used. In this study, the three-dimensional (3D) through-time radial generalized autocalibrating partially parallel acquisition (GRAPPA) method is used to reconstruct highly accelerated stack-of-stars data for time-resolved renal MRAs.
Through-time radial GRAPPA has been recently introduced as a method for non-Cartesian GRAPPA weight calibration, and a similar concept can also be used in 3D acquisitions. By combining different sources of calibration information, acquisition time can be reduced. Here, different GRAPPA weight calibration schemes are explored in simulation, and the results are applied to reconstruct undersampled stack-of-stars data.
Simulations demonstrate that an accurate and efficient approach to 3D calibration is to combine a small number of central partitions with as many temporal repetitions as exam time permits. These findings were used to reconstruct renal trMRA data with an in-plane acceleration factor as high as 12.6 with respect to the Nyquist sampling criterion, where the lowest root mean squared error value of 16.4% was achieved when using a calibration scheme with 8 partitions, 16 repetitions, and a 4 projection × 8 read point segment size.
3D through-time radial GRAPPA can be used to successfully reconstruct highly accelerated non-Cartesian data. By using in-plane radial undersampling, a trMRA can be acquired with a temporal footprint less than 4s/frame with a spatial resolution of approximately 1.5 mm × 1.5 mm × 3 mm.
为实现对比增强时间分辨磁共振血管造影检查(trMRA)的高时间和空间分辨率,必须使用非笛卡尔并行成像等快速成像技术。在本研究中,采用三维(3D)逐时径向广义自校准部分并行采集(GRAPPA)方法来重建用于时间分辨肾脏MRA的高度加速的星状堆叠数据。
逐时径向GRAPPA最近被引入作为一种非笛卡尔GRAPPA权重校准方法,类似的概念也可用于3D采集。通过组合不同的校准信息源,可以减少采集时间。在此,在模拟中探索了不同的GRAPPA权重校准方案,并将结果应用于重建欠采样的星状堆叠数据。
模拟表明,一种准确且高效的3D校准方法是将少量中心分区与检查时间允许的尽可能多的时间重复相结合。这些发现被用于重建肾脏trMRA数据,相对于奈奎斯特采样标准,面内加速因子高达12.6,当使用具有8个分区、16次重复和4投影×8读取点段大小的校准方案时,实现了16.4%的最低均方根误差值。
3D逐时径向GRAPPA可用于成功重建高度加速的非笛卡尔数据。通过使用面内径向欠采样,可以采集时间占用小于4s/帧、空间分辨率约为1.5mm×1.5mm×3mm的trMRA。