Principal Component Analysis Based Dynamic Cone Beam X-Ray Luminescence Computed Tomography: A Feasibility Study

Cone beam X-ray luminescence computed tomography (CB-XLCT) is a promising imaging technique in studying the physiological and pathological processes in small animals. However, the dynamic bio-distributions of probes in small animal, especially in adjacent targets are still hard to be captured directly from dynamic CB-XLCT. In this paper, a 4D temporal-spatial reconstruction method based on principal component analysis (PCA) in the projection space is proposed for dynamic CB-XLCT. First, projections of angles in each 3D frame are compressed to reduce the noises initially. Then a temporal PCA is performed on the projection data to decorrelate the 4D problem into separate 3D problems in the PCA domain. In the PCA domain, the difference between dynamic behaviors of multiple targets can be reflected by the first several principal components which can be further used for fast and improved reconstruction by a restarted Tikhonov regularization method. At last, by discarding the principal components mainly reflecting noise, the concentration series of targets are recovered from the first few reconstruction results with a mask as the constraint. The numerical simulation and phantom experiment demonstrate that the proposed method can resolve multiple targets and recover the dynamic distributions with high computation efficiency. The proposed method provides new feasibility for imaging dynamic bio-distributions of probes in vivo.