Zhang Haibo, Huang Xiaodong, Zhou Mingquan, Geng Guohua, He Xiaowei
School of Information Sciences and Technology, Northwest University, Xi'an, Shannxi 710027, China.
Baoji Central Hospital, Baoji, Shannxi 710127, China.
Biomed Opt Express. 2020 Jun 12;11(7):3717-3732. doi: 10.1364/BOE.393970. eCollection 2020 Jul 1.
Cone-beam X-ray luminescence computed tomography (CB-XLCT) emerged as a novel hybrid technique for early detection of small tumors . However, severe ill-posedness is still a challenge for CB-XLCT imaging. In this study, an adaptive shrinking reconstruction framework without information is proposed for CB-XLCT. In reconstruction processing, the mesh nodes are automatically selected with higher probability to contribute to the distribution of target for imaging. Specially, an adaptive shrinking function is designed to automatically control the permissible source region at a multi-scale rate. Both 3D digital mouse and experiments were carried out to test the performance of our method. The results indicate that the proposed framework can dramatically improve the imaging quality of CB-XLCT.
锥形束X射线发光计算机断层扫描（CB-XLCT）作为一种用于早期检测小肿瘤的新型混合技术应运而生。然而，严重的不适定性仍然是CB-XLCT成像面临的一个挑战。在本研究中，针对CB-XLCT提出了一种无信息的自适应收缩重建框架。在重建过程中，网格节点被自动以更高的概率选择，以有助于成像目标的分布。特别地，设计了一种自适应收缩函数，以多尺度速率自动控制允许的源区域。进行了三维数字小鼠实验和实际实验来测试我们方法的性能。结果表明，所提出的框架可以显著提高CB-XLCT的成像质量。
