Computed tomography (CT) relies on precise patient immobilization during image acquisition. Nevertheless, motion artifacts in the reconstructed images can persist. Motion compensation methods aim to correct such artifacts post-acquisition, often incorporating temporal smoothness constraints on the estimated motion patterns. This study analyzes the influence of a spline-based motion model within an existing rigid motion compensation algorithm for cone-beam CT on the recoverable motion frequencies. Results demonstrate that the choice of motion model crucially influences recoverable frequencies. The optimization-based motion compensation algorithm is able to accurately fit the spline nodes for frequencies almost up to the node-dependent theoretical limit according to the Nyquist-Shannon theorem. Notably, a higher node count does not compromise reconstruction performance for slow motion patterns, but can extend the range of recoverable high frequencies for the investigated algorithm. Eventually, the optimal motion model is dependent on the imaged anatomy, clinical use case, and scanning protocol and should be tailored carefully to the expected motion frequency spectrum to ensure accurate motion compensation.

翻译：计算机断层扫描（CT）依赖于图像采集过程中患者的精确固定。然而，重建图像中的运动伪影仍可能持续存在。运动补偿方法旨在在采集后校正此类伪影，通常对估计的运动模式施加时间平滑性约束。本研究分析了基于样条的运动模型在现有锥束CT刚性运动补偿算法中对可恢复运动频率的影响。结果表明，运动模型的选择对可恢复频率具有关键影响。基于优化的运动补偿算法能够精确拟合样条节点，其频率几乎达到节点依赖的奈奎斯特-香农定理理论极限。值得注意的是，对于缓慢运动模式，增加节点数量不会损害重建性能，但可以扩展所研究算法的可恢复高频范围。最终，最优运动模型取决于成像解剖结构、临床使用场景和扫描协议，应针对预期的运动频谱进行仔细定制，以确保准确的运动补偿。