康普顿相机的有界延迟球直方图重建
Bounded-Latency Spherical-Histogram Reconstruction for Compton Cameras
AI总结：
针对康普顿相机伽马射线成像计算量大的问题，提出球直方图框架，通过预计算稀疏投影算子实现有界延迟重建，支持多视图等多种操作。
AI中文摘要：
康普顿相机的伽马射线成像计算量大，因为传统重建在反演循环中保留列表模式采集：随着事件数增加，必须重新计算依赖事件的锥/体素相互作用。我们提出一种球直方图框架，每个康普顿事件在线编码为以探测器为中心的角直方图，使用预计算的稀疏投影算子从相干直方图快照执行体积重建。该方法将事件流转化为有界重建状态，解耦事件积累与迭代反演，支持多视图、多分辨率操作、非阻塞采集及迭代正/反演重建，其主要成本取决于[原文未明确的后续计算环节]。
英文摘要：
Gamma-ray imaging with Compton cameras is computationally demanding because conventional reconstruction retains the list-mode acquisition inside the inversion loop: event-dependent cone/voxel interactions must be recomputed as the event count grows. We present a spherical-histogram framework in which each Compton event is encoded online into detector-centred angular histograms. Volumetric reconstruction is then performed from coherent histogram snapshots using a precomputed sparse projection operator. This turns the event stream into a bounded reconstruction state, decoupling event accumulation from iterative inversion. The method supports multi-view and multi-resolution operation, non-blocking acquisition, and iterative forward/backward reconstruction whose dominant cost depends.
