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

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.