Adaptive shrinking reconstruction framework for cone-beam X-ray luminescence computed tomography

Haibo Zhang; Xiaodong Huang; Mingquan Zhou; Guohua Geng; Xiaowei He

doi:10.1364/BOE.393970

Adaptive shrinking reconstruction framework for cone-beam X-ray luminescence computed tomography

Biomed Opt Express. 2020 Jun 12;11(7):3717-3732. doi: 10.1364/BOE.393970. eCollection 2020 Jul 1.

Authors

Haibo Zhang¹, Xiaodong Huang², Mingquan Zhou¹, Guohua Geng¹, Xiaowei He¹

Affiliations

¹ School of Information Sciences and Technology, Northwest University, Xi'an, Shannxi 710027, China.
² Baoji Central Hospital, Baoji, Shannxi 710127, China.

Abstract

Cone-beam X-ray luminescence computed tomography (CB-XLCT) emerged as a novel hybrid technique for early detection of small tumors in vivo. However, severe ill-posedness is still a challenge for CB-XLCT imaging. In this study, an adaptive shrinking reconstruction framework without a prior 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 in vivo 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.