IEEE Trans Med Imaging. 2018 Dec;37(12):2603-2612. doi: 10.1109/TMI.2018.2844246. Epub 2018 Jun 5.
We present a method of generating spatial maps of kinetic parameters from dynamic sequences of images collected in hyperpolarized carbon-13 magnetic resonance imaging (MRI) experiments. The technique exploits spatial correlations in the dynamic traces via regularization in the space of parameter maps. Similar techniques have proven successful in other dynamic imaging problems, such as dynamic contrast enhanced MRI. In this paper, we apply these techniques for the first time to hyperpolarized MRI problems, which are particularly challenging due to limited signal-to-noise ratio (SNR). We formulate the reconstruction as an optimization problem and present an efficient iterative algorithm for solving it based on the alternation direction method of multipliers. We demonstrate that this technique improves the qualitative appearance of parameter maps estimated from low SNR dynamic image sequences, first in simulation then on a number of data sets collected in vivo. The improvement this method provides is particularly pronounced at low SNR levels.
我们提出了一种从极化碳-13 磁共振成像（MRI）实验中采集的动态图像序列生成动力学参数空间图谱的方法。该技术通过在参数图谱的空间中进行正则化来利用动态轨迹中的空间相关性。类似的技术已在其他动态成像问题中证明是成功的，例如动态对比增强 MRI。在本文中，我们首次将这些技术应用于极化 MRI 问题，由于信噪比（SNR）有限，这些问题特别具有挑战性。我们将重建表述为一个优化问题，并提出了一种基于交替方向乘子法的有效迭代算法来求解它。我们证明，该技术可改善从低 SNR 动态图像序列中估计的参数图谱的定性外观，首先在模拟中，然后在体内收集的多个数据集上进行演示。该方法在低 SNR 水平下提供的改善尤为显著。
