Direct prior regularization from anatomical images for cone beam X-ray luminescence computed tomography reconstruction

Cone beam X-ray luminescence computed tomography (CB-XLCT) has recently been proposed as a new imaging modality for biological imaging application. Compared with other XLCT systems such as pencil beam XLCT and narrow beam XLCT, CB-XLCT can achieve fast imaging, where the speed is essential to small animal in vivo imaging studies. However, due to the high degree of light scattering in biological tissues, the CB-XLCT reconstruction is an ill-posed problem, which can result in poor image quality such as low spatial resolution. As a hybrid CT/optical imaging technique, the image quality is conjected to be improved substantially with the structural guidance from the anatomical images of the CT component. For that purpose, in this paper, a direct prior regularization method is proposed by introducing anatomical information directly into the CB-XLCT reconstruction. The primary advantage of the proposed method is that it does not require segmentation of targets in the anatomical images. Phantom experiments with different edge-to-edge distance (EED) were performed to realize the proposed approach's feasibility. Phantom experiments results indicate that the proposed direct regularization method can separate two luminescent targets with an EED of 0 mm. Compared with no-prior reconstruction methods such as ART and adaptive Tikhonov methods, the proposed method can significantly improve the imaging resolution of CB-XLCT.