Principal Component Analysis Based Dynamic Cone Beam X-Ray Luminescence Computed Tomography: A Feasibility Study
Principal Component Analysis Based Dynamic Cone Beam X-Ray Luminescence Computed Tomography: A Feasibility Study
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DOI：
10.1109/tmi.2019.2917026
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发表时间：
2019-12-01
影响因子：
10.6
通讯作者：
Lu, Hongbing
中科院分区：
文献类型：
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作者：
Pu, Huangsheng;Gao, Peng;Lu, Hongbing
锥束x射线发光计算机断层扫描（CB-XLCT）是研究小动物生理和病理过程的一种很有前途的成像技术。然而，从动态CB-XLCT中直接捕捉小动物中探针的动态生物分布，特别是邻近目标的动态生物分布仍然很困难。本文提出了一种基于投影空间主成分分析（PCA）的动态CB-XLCT四维时空重建方法。首先，对每个3D帧的角度投影进行压缩，初步降低噪声；然后对投影数据进行时间主成分分析，在主成分分析域中将四维问题分解为独立的三维问题。在PCA域，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，以掩模为约束，从前几次重建结果中恢复目标的浓度序列。数值模拟和仿真实验表明，该方法能有效地分解多个目标，恢复目标的动态分布，计算效率高。该方法为探针在体内的动态生物分布成像提供了新的可行性。
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 probesin 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 easibility for imaging dynamic bio-distributions of probes in vivo.