Reconstructing a dynamic object with affine motion in computerized tomography (CT) leads to motion artifacts if the motion is not taken into account. In most cases, the actual motion is neither known nor can be determined easily. As a consequence, the respective model that describes CT is incomplete. The iterative RESESOP-Kaczmarz method can - under certain conditions and by exploiting the modeling error - reconstruct dynamic objects at different time points even if the exact motion is unknown. However, the method is very time-consuming. To speed the reconstruction process up and obtain better results, we combine the following three steps: 1. RESESOP-Kacmarz with only a few iterations is implemented to reconstruct the object at different time points. 2. The motion is estimated via landmark detection, e.g. using deep learning. 3. The estimated motion is integrated into the reconstruction process, allowing the use of dynamic filtered backprojection. We give a short review of all methods involved and present numerical results as a proof of principle.

翻译：计算机断层成像中，若未考虑带仿射运动的动态物体的重建，将导致运动伪影。在大多数情况下，实际运动既未知又难以确定，导致描述CT的相应模型不完整。迭代RESESOP-Kaczmarz方法在特定条件下，通过利用建模误差，可在精确运动未知的情况下重建不同时间点的动态物体。然而，该方法极为耗时。为加速重建过程并获得更优结果，我们结合以下三个步骤：1. 采用仅需少量迭代的RESESOP-Kaczmarz方法重建不同时间点的物体；2. 通过地标检测（如深度学习）估计运动；3. 将估计的运动整合到重建过程中，从而支持动态滤波反投影。我们简要综述了涉及的所有方法，并给出数值结果作为原理验证。