IEEE Trans Biomed Eng. 2022 Jan;69(1):229-243. doi: 10.1109/TBME.2021.3091881. Epub 2021 Dec 23.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a tissue perfusion imaging technique. Some versatile free-breathing DCE-MRI techniques combining compressed sensing (CS) and parallel imaging with golden-angle radial sampling have been developed to improve motion robustness with high spatial and temporal resolution. These methods have demonstrated good diagnostic performance in clinical setting, but the reconstruction quality will degrade at high acceleration rates and overall reconstruction time remains long. In this paper, we proposed a new parallel CS reconstruction model for DCE-MRI that enforces flexible weighted sparse constraint along both spatial and temporal dimensions. Weights were introduced to flexibly adjust the importance of time and space sparsity, and we derived a fast-thresholding algorithm which was proven to be simple and efficient for solving the proposed reconstruction model. Results on both the brain tumor DCE and liver DCE show that, at relatively high acceleration factor of fast sampling, lowest reconstruction error and highest image structural similarity are obtained by the proposed method. Besides, the proposed method achieves faster reconstruction for liver datasets and better physiological measures are also obtained on tumor images.
动态对比增强磁共振成像（DCE-MRI）是一种组织灌注成像技术。一些功能强大的自由呼吸 DCE-MRI 技术结合压缩感知（CS）和并行成像与黄金角度径向采样，以提高运动鲁棒性和高空间和时间分辨率。这些方法在临床环境中表现出良好的诊断性能，但在高加速率下重建质量会下降，整体重建时间仍然较长。在本文中，我们提出了一种新的 DCE-MRI 并行 CS 重建模型，该模型在空间和时间两个维度上都施加了灵活的加权稀疏约束。引入权重来灵活调整时间和空间稀疏性的重要性，并且我们推导出了一种快速阈值算法，该算法被证明对于解决所提出的重建模型非常简单和高效。在脑肿瘤 DCE 和肝脏 DCE 上的结果表明，在相对较高的快速采样加速因子下，所提出的方法可以获得最低的重建误差和最高的图像结构相似性。此外，该方法还实现了肝脏数据集的快速重建，并在肿瘤图像上获得了更好的生理测量值。
