发表机构
Friedrich-Alexander-Universität Erlangen-Nürnberg; Deggendorf Institute of Technology（埃尔朗根-纽伦堡弗里德里希-亚历山大大学; 德根多夫技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出将稀疏视角锥束CT的每个源位姿作为连续变量，通过梯度上升联合优化轨迹，集成软Tuy覆盖、视角协方差损失与衰减惩罚，在多种扫描几何上提升缺陷可见性并支持快速预扫描规划。
AI 中文摘要
锥束计算机断层扫描（CT）的轨迹优化决定了稀疏视角扫描所获取的信息。固定的候选池阻碍了网格外的细化，并且需要针对每个采集流形进行新的对象特定预计算。我们将每个源位姿设为独立的连续变量，并通过在扫描仪的动力学流形上进行梯度上升来联合移动所有位姿。目标函数结合了软Tuy平面覆盖、连续视角协方差损失以及解析的衰减感知射线束惩罚。同一优化器可处理圆形、有限C臂、双轴和自由球参数化。在Defrise法兰上，连续选择恢复了圆形轨道无法看到的层状缺陷，在更稀疏的预算下与自由球上的离散交换搜索相匹配，并在更密集的预算下领先，每个分支均评估相同目标。适度的仰角带已能恢复缺陷处的大部分自由球增益，因此同一优化器可迁移至有界扫描仪包络。光子噪声保持了法兰上的排序，并在密集燃料喷嘴上压缩了排序。稀疏预扫描规划受益于预扫描与规划采集流形的匹配。选择耗时数秒而非数分钟，无需对象特定的重建基。预扫描规划的位姿在机器人CT台上执行，并在公共框架中重建，证明了可行性，但相对于均匀带采样没有一致的指标增益。连续位姿优化将衰减和扫描仪约束直接纳入稀疏视角采集设计中。
英文摘要
Trajectory optimisation for cone-beam computed tomography (CT) determines which information sparse-view scans acquire. Fixed candidate pools prevent off-grid refinement and require new object-specific precomputation for each acquisition manifold. We make every source pose an individual continuous variable and move all poses jointly by gradient ascent on the scanner's kinematic manifold. The objective combines soft-Tuy plane coverage, continuous View Covariance Loss, and an analytic attenuation-aware ray-bundle penalty. The same optimiser handles circular, limited C-arm, two-axis, and freesphere parametrisations. On a Defrise flange, continuous selection recovers laminar defects invisible to a circular orbit, matches discrete swap search on the free sphere at the sparser budget, and leads at the denser one, with the same objective evaluated in every arm. A moderate elevation band already recovers most of the free-sphere gain at the defects, so the same optimiser transfers to bounded scanner envelopes. Photon noise preserves the ordering on the flange and compresses it on a dense fuel nozzle. Sparseprescan planning benefits from matching prescan and planned acquisition manifolds. Selection takes seconds rather than minutes without an object-specific reconstruction basis. Prescan-planned poses were executed on a robot CT bench and reconstructed in a common frame, demonstrating feasibility but no consistent metric gain over uniform band sampling. Continuous pose optimisation incorporates attenuation and scanner constraints directly into sparse-view acquisition design.
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