Institute of Medical Physics (IMP), University of Erlangen-Nürnberg, Henkestrasse. 91, 91052 Erlangen, Germany.
Phys Med Biol. 2012 Mar 21;57(6):1517-25. doi: 10.1088/0031-9155/57/6/1517. Epub 2012 Mar 5.
Temporal-correlated image reconstruction, also known as 4D CT image reconstruction, is a big challenge in computed tomography. The reasons for incorporating the temporal domain into the reconstruction are motions of the scanned object, which would otherwise lead to motion artifacts. The standard method for 4D CT image reconstruction is extracting single motion phases and reconstructing them separately. These reconstructions can suffer from undersampling artifacts due to the low number of used projections in each phase. There are different iterative methods which try to incorporate some a priori knowledge to compensate for these artifacts. In this paper we want to follow this strategy. The cost function we use is a higher dimensional cost function which accounts for the sparseness of the measured signal in the spatial and temporal directions. This leads to the definition of a higher dimensional total variation. The method is validated using in vivo cardiac micro-CT mouse data. Additionally, we compare the results to phase-correlated reconstructions using the FDK algorithm and a total variation constrained reconstruction, where the total variation term is only defined in the spatial domain. The reconstructed datasets show strong improvements in terms of artifact reduction and low-contrast resolution compared to other methods. Thereby the temporal resolution of the reconstructed signal is not affected.
时相关图像重建，也称为 4D CT 图像重建，是计算机断层扫描中的一个重大挑战。将时域纳入重建的原因是扫描物体的运动，否则会导致运动伪影。4D CT 图像重建的标准方法是提取单个运动相位并分别重建它们。由于每个相位中使用的投影数量较少，这些重建可能会受到欠采样伪影的影响。有不同的迭代方法试图结合一些先验知识来补偿这些伪影。在本文中，我们希望遵循这一策略。我们使用的代价函数是一个高维代价函数，它考虑了测量信号在空间和时间方向上的稀疏性。这导致了高维全变差的定义。该方法使用体内心脏微 CT 小鼠数据进行了验证。此外，我们还将结果与使用 FDK 算法和全变差约束重建的相位相关重建进行了比较，其中全变差项仅在空间域中定义。与其他方法相比，重建数据集在减少伪影和提高低对比度分辨率方面有了显著的改善。因此，重建信号的时间分辨率不受影响。
