生物发光断层成像(BLT)是一种非侵入、高灵敏度的光学分子影像技术,可以通过探测生物体表面的光信号重建出生物体内部光源的三维分布情况。由于光在组织中传播时,散射占据主导作用,导致BLT重建问题的病态性,给光源重建带来巨大的挑战。在BLT重建中,基于光源稀疏分布的特征,稀疏正则化方法相比于传统的L2范数正则化取得了显著进展。更进一步,由于生物发光光源的分布具有的空间聚集特征,利用该特征将有助于进一步提高BLT重建的准确性。相比于传统的针对求解域中所有未知量进行稀疏重建的算法,探索了利用块稀疏进行生物发光断层成像重建的可行性,首先通过对系统矩阵进行相关系数分析将求解域划分成一系列数据块,然后利用块稀疏贝叶斯算法对生物发光光源的分布进行三维重建。通过仿真实验与小鼠活体实验,并与传统稀疏重建算法L1-LS进行了比较,结果表明该方法可以有效缓解BLT重建问题的病态性,抑制噪声,并且可提高重建结果的准确性。

Bioluminescence tomography (BLT) is a non-invasive, highly sensitive optical molecular imaging technique, which can reveal the three-dimensional (3D) distribution of the bioluminescent sources inside the tissue through the light signals detected on the surface. The BLT reconstruction problem is ill-posed due to the domination of scattering during the light propagation through the tissue, which results in a challenge to accurately reveal the 3D source distribution. According to the sparse distribution of the bioluminescent sources, the sparse regularization method based on L1 norm has achieved a significant improvement comparing to the traditional L2 norm regularization. Furthermore, due to the spatial aggregation characteristics of the bioluminescent light sources, adopting this feature would further improve the BLT reconstruction accuracy. Comparing to the traditional sparse reconstruction algorithm which takes all unknowns in the solution domain into account, the feasibility of block sparse priori information used for the BLT reconstruction is explored. First, the solution domain is divided into a series of data blocks through analyzing the correlation coefficient between the columns of the system matrix. Then, the block sparse Bayes learning algorithm is used to reconstruct the distribution of the bioluminescent sources. Through the simulation experiment and the mouse in vivo experiment, and compared with those by the traditional sparse reconstruction algorithm based on L1-LS, the results show that the proposed method can effectively alleviate the ill-posedness of the BLT reconstruction problem, suppress noise, and improve the reconstruction accuracy.