Institute for Medical Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany.
Institute for Analysis and Numerics, Otto von Guericke-University Magdeburg, Magdeburg, Germany.
Med Phys. 2018 Mar;45(3):1080-1092. doi: 10.1002/mp.12768. Epub 2018 Feb 19.
The issue of perfusion imaging using a temporal decomposition model is to enable the reconstruction of undersampled measurements acquired with a slowly rotating x-ray-based imaging system, for example, a C-arm-based cone beam computed tomography (CB-CT). The aim of this work is to integrate prior knowledge into the dynamic CT task in order to reduce the required number of views and the computational effort as well as to save dose. The prior knowledge comprises of a mathematical model and clinical perfusion data.
In case of model-based perfusion imaging via superposition of specified orthogonal temporal basis functions, a priori knowledge is incorporated into the reconstructions. Instead of estimating the dynamic attenuation of each voxel by a weighting sum, the modeling approach is done as a preprocessing step in the projection space. This point of view provides a method that decomposes the temporal and spatial domain of dynamic CT data. The resulting projection set consists of spatial information that can be treated as individual static CT tasks. Consequently, the high-dimensional model-based CT system can be completely transformed, allowing for the use of an arbitrary reconstruction algorithm.
For CT, reconstructions of preprocessed dynamic in silico data are illustrated and evaluated by means of conventional clinical parameters for stroke diagnostics. The time separation technique presented here, provides the expected accuracy of model-based CT perfusion imaging. Consequently, the model-based handled 4D task can be solved approximately as fast as the corresponding static 3D task.
For C-arm-based CB-CT, the algorithm presented here provides a solution for resorting to model-based perfusion reconstruction without its connected high computational cost. Thus, this algorithm is potentially able to have recourse to the benefit from model-based perfusion imaging for practical application. This study is a proof of concept.
使用时间分解模型进行灌注成像的问题是为了能够重建使用基于旋转缓慢的 X 射线成像系统（例如基于 C 臂的锥形束 CT（CB-CT））采集的欠采样测量值。这项工作的目的是将先验知识整合到动态 CT 任务中，以减少所需的视图数量和计算工作量，并节省剂量。先验知识包括数学模型和临床灌注数据。
在基于模型的灌注成像中，通过叠加指定的正交时间基础函数，可以将先验知识纳入重建中。在投影空间中进行预处理步骤，而不是通过加权和来估计每个体素的动态衰减，建模方法可以代替。这种观点提供了一种将动态 CT 数据的时间和空间域分解的方法。所得的投影集包含可以作为单独的静态 CT 任务进行处理的空间信息。因此，可以完全转换高维基于模型的 CT 系统，从而可以使用任意重建算法。
对于 CT，通过使用中风诊断的常规临床参数来说明和评估预处理的动态模拟数据的重建。这里提出的时间分离技术提供了基于模型的 CT 灌注成像的预期准确性。因此，基于模型的 4D 任务可以近似快速地解决，就像对应的静态 3D 任务一样。
对于基于 C 臂的 CB-CT，这里提出的算法为基于模型的灌注重建提供了一种解决方案，而无需其相关的高计算成本。因此，该算法有可能在实际应用中受益于基于模型的灌注成像。本研究是概念验证。
