为了缓解单视图X射线发光断层成像中出现的不适定性问题, 提出了一种结合区域迭代收缩策略的快速贝叶斯匹配追踪方法。该方法将贝叶斯模型和贪婪算法相结合, 可以从较少的观测值中高效快速地恢复稀疏信号。为了进一步提高重建精度, 将快速贝叶斯追踪与区域迭代收缩策略结合, 简化了自适应有限元方法在网格划分和系统矩阵构建方面的复杂性, 在缩减因子迭代缩小可行区域的同时, 缓解了X射线发光断层成像逆向题求解中的病态性。为评估该方法的有效性, 设计了仿真实验与真实物理仿体实验。仿真结果表明, 本文方法在加快重建速度的同时, 显著提高了纳米发光目标的定位精度和发光产额的定量结果, 它们分别为0.73 mm和0.79 μg。真实物理仿体实验进一步验证了该方法在实际单视图X射线发光断层成像中的可行性。

To relieve the ill-posedness of single-view x-ray luminescence computed tomography (XLCT), a fast Bayesian matching pursuit (FBMP) method combined with iterative-shrinking permissible region (ISPR) strategy was put forward.In this method, Bayesian model was combined with the greedy algorithm to quickly and efficiently restore sparse signal from few observed values. To further improve the reconstruction accuracy, FBMP was combined with ISPR strategy to simplify the mesh generation and system matrix construction by self-adaptive finite element, downsizing the factor iterative-shrinking permissible region, meanwhile relieving the ill-posedness of XLCT in reverse solution. In order to verify the effectiveness of the method, a simulation and a real physical phantom experiment were performed. The simulation results show that the proposed method, while speeding up the reconstruction process, significantly improve the localization accuracy of nano luminescent target and quantitative result of luminescence yield, which are 0.73 mm and 0.79 μg respectively. The physical phantom experiment further verifies the feasibility of this method in actual XLCT.