发表机构
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany（模式识别实验室，弗赖堡-亚历山大-大学埃尔朗根-纽伦堡，埃尔朗根，德国）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究锥束CT中可微变移位FBP在挑战性采集设置下的鲁棒性与稳定性，通过系统研究发现其在不规则轨迹稳定，采样点分布影响大，稀疏视图下质量优、计算快，严重欠采样有性能下降，还适用于非平面多等中心几何结构。
AI 中文摘要
可微变移位滤波反投影（SV-FBP）框架能够在一般源轨迹下对锥束CT重建的数据驱动冗余权重进行估计，无需解析推导加权方案。本文对可微SV-FBP在具有挑战性的采集设置下的鲁棒性和适应性进行了系统研究。结果表明该框架在高度不规则和不连续轨迹上保持稳定，采样点空间分布起更主导作用。在稀疏视图条件下，可微SV-FBP重建质量有竞争力，计算时间大幅减少。但在严重欠采样时有性能下降。此外，该框架适用于非平面多等中心几何结构。这些发现为可微SV-FBP模型行为和局限性提供新见解，凸显其在非标准和机器人CBCT采集场景中的灵活性和高效性。
英文摘要
The differentiable shift-variant filtered backprojection (SV-FBP) framework enables data-driven estimation of redundancy weights for cone-beam CT reconstruction under general source trajectories, removing the need for analytically derived weighting schemes. In this work, we present a systematic study of the robustness and adaptability of differentiable SV-FBP under challenging acquisition settings. We show that the framework remains stable across highly irregular and discontinuous trajectories, indicating that reconstruction performance is largely insensitive to trajectory ordering or continuity. Instead, the spatial distribution of sampling points plays a more dominant role. Under sparse-view conditions, differentiable SV-FBP achieves competitive reconstruction quality while providing an order-of-magnitude reduction in computation time compared to iterative reconstruction methods at moderate sampling densities. However, we identify a clear transition regime under severe undersampling, where the absence of iterative data consistency leads to performance degradation. Furthermore, we demonstrate that the framework remains applicable to non-planar multi-isocenter geometries, such as Lissajous-saddle trajectories, without requiring architectural modifications. These findings provide new insights into the behavior and limitations of the differentiable SV-FBP model and highlight it as a flexible and efficient solution for non-standard and robotic CBCT acquisition scenarios.
CommentsAccepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2026:014
Journal refMachine.Learning.for.Biomedical.Imaging. 2026 (2026)
DOI：10.59275/j.melba.2026-252c
