IEEE Trans Med Imaging. 2019 Dec;38(12):2891-2902. doi: 10.1109/TMI.2019.2917026. Epub 2019 May 15.
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.
锥形束 X 射线发光计算机断层扫描 (CB-XLCT) 是一种很有前途的小动物体内生理和病理过程研究成像技术。然而，小动物（特别是相邻靶标）中探针的动态生物分布仍然难以直接从动态 CB-XLCT 中捕捉到。在本文中，提出了一种基于投影空间主成分分析 (PCA) 的 4D 时-空重建方法，用于动态 CB-XLCT。首先，对每一帧的角度投影进行压缩，以初步降低噪声。然后在投影数据上进行时间 PCA，将 4D 问题解相关到 PCA 域中的独立 3D 问题。在 PCA 域中，多个目标的动态行为差异可以通过前几个主成分来反映，这些主成分可以进一步用于通过重新启动的 Tikhonov 正则化方法进行快速和改进的重建。最后，通过丢弃主要反映噪声的主成分，从前几次重建结果中使用掩模作为约束来恢复目标的浓度序列。数值模拟和体模实验表明，该方法可以解析多个目标并以高计算效率恢复动态分布。该方法为体内探针的动态生物分布成像提供了新的可行性。
