Rapid 3D dynamic arterial spin labeling with a sparse model-based image reconstruction.
Rapid 3D dynamic arterial spin labeling with a sparse model-based image reconstruction.
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DOI：
10.1016/j.neuroimage.2015.07.018
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发表时间：
2015-11-01
期刊：
影响因子：
5.7
通讯作者：
Meyer CH
中科院分区：
文献类型：
--
作者：
Zhao L;Fielden SW;Feng X;Wintermark M;Mugler JP 3rd;Meyer CH
动态动脉自旋标记（ASL）MRI在多个观察时间测量灌注团，并在动脉通过时间存在变化的情况下准确估计脑血流。ASL具有固有的低信噪比（SNR）并且对运动敏感，因此通常需要广泛的信号平均，导致动态ASL的扫描时间长。本研究的目标是开发一种加速动态ASL方法，该方法使用基于模型的图像重建，利用动态ASL数据的固有稀疏性，具有改善的SNR和对运动的鲁棒性。该方法的第一部分是使用并行成像和压缩感测的组合加速的单发3D涡轮自旋回波螺旋脉冲序列。然后将该脉冲序列并入在多个观察时间采集的动态伪连续ASL采集中，并联合重建所得图像，以执行潜在灌注时间过程的模型。该技术的性能进行了验证，使用数字体模和验证正常志愿者的3特斯拉扫描仪。在仿真中，空间稀疏性约束提高了信噪比并减少了估计误差。结合基于模型的稀疏约束，该方法进一步提高了信噪比，减小了估计误差，抑制了运动伪影。在实验中，所提出的方法产生了显着的改进，扫描时间短至每个时间点20秒。这些结果表明，基于模型的图像重建，使快速的动态ASL具有更高的精度和鲁棒性。
Dynamic arterial spin labeling (ASL) MRI measures the perfusion bolus at multiple observation times and yields accurate estimates of cerebral blood flow in the presence of variations in arterial transit time. ASL has intrinsically low signal-to-noise ratio (SNR) and is sensitive to motion, so that extensive signal averaging is typically required, leading to long scan times for dynamic ASL. The goal of this study was to develop an accelerated dynamic ASL method with improved SNR and robustness to motion using a model-based image reconstruction that exploits the inherent sparsity of dynamic ASL data. The first component of this method is a single-shot 3D turbo spin echo spiral pulse sequence accelerated using a combination of parallel imaging and compressed sensing. This pulse sequence was then incorporated into a dynamic pseudo continuous ASL acquisition acquired at multiple observation times, and the resulting images were jointly reconstructed enforcing a model of potential perfusion time courses. Performance of the technique was verified using a numerical phantom and validated on normal volunteers on a 3-Tesla scanner. In simulation, a spatial sparsity constraint improved SNR and reduced estimation errors. Combined with a model-based sparsity constraint, the proposed method further improved SNR, reduced estimation error and suppressed motion artifacts. Experimentally, the proposed method resulted in significant improvements, with scan times as short as 20 seconds per time point. These results suggest that the model-based image reconstruction enables rapid dynamic ASL with improved accuracy and robustness.