一种优化区域知识先验的稀疏角锥束X射线发光断层成像方法

Sparse-View Cone-Beam X-Ray Luminescence Computed Tomography Imaging for Optimized Regional Prior Knowledge

锥束X射线发光断层(CB-XLCT)成像是一种可在生物体外对早期肿瘤进行有效检测的新型医学成像技术。稀疏角CB-XLCT成像加速了CB-XLCT技术的实时成像转化进程。然而,相对于传统多角度成像,稀疏角CB-XLCT成像的逆问题病态性明显加剧,这对传统成像方法的有效扩展提出了挑战。基于稀疏非凸L_(p)(0<p<1)模型,提出一种迭代重加权裂分增广拉格朗日收缩算法,进一步结合经典非凸算子提出一种鲁棒稳定的可行区域提取方法,进而作为优化的知识先验指导靶标的准确重建。设计了数字鼠和物理仿体实验,分别结合经典L_(1)范数和L_(0)范数的代表算法验证所提方法的有效性和稳健性。实验结果表明,所提方法不仅可有效求解稀疏角CBXLCT成像逆问题,还具有良好的可扩展性。

Cone-beam X-ray luminescence computed tomography(CB-XLCT)imaging is a new medical imaging technique that can effectively detect early tumors in vitro.Sparse-view CB-XLCT imaging brings this technology a step closer to real-time imaging of CB-XLCT technique.Nevertheless,it suffers from a much more severe ill-conditioned inverse problem compared with traditional multi-view imaging,which poses a challenge to the extension of the traditional approach.A sparse non-convex L_(p)(0<p<1)model was employed to formulate an iteratively reweighted splitting augmented Lagrangian shrinkage algorithm.The classic non-convex operator was then combined with the algorithm to design a robust and steady method of extracting permissible regions so that it could guide the accurate reconstruction of the target as optimized prior knowledge.Digital rat and physical phantom experiments were designed and combined respectively with the classic representative algorithms of the L_(1)-norm and L_(0)-norm to evaluate the efficiency and robustness of the proposed method.The experimental results demonstrate that our proposed method can effectively solve the inverse problem of sparse-view CB-XLCT imaging and possesses expandability.