Source reconstruction for bioluminescence tomography via L1/2 regularization
Source reconstruction for bioluminescence tomography via L1/2 regularization
复制标题
通过 L1/2 正则化进行生物发光断层扫描的源重建
DOI：
10.1142/s1793545817500146
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发表时间：
2017
影响因子：
2.5
通讯作者：
王海玉
中科院分区：
文献类型：
--
作者：
余景景;李启越;王海玉
生物发光断层成像(BLT)是临床前研究中一种重要的非侵入性光学分子成像手段。为了改善图像质量，重建算法必须处理BLT逆问题固有的不适定性。生物发光源在空间分布上的稀疏性在BLT中得到了广泛的研究，由于L1范数的稀疏性，许多L1正则化方法也被研究出来。本文提出了一种基于L1/2正则化的重构方法来增强BLT解的稀疏性，并将非凸L1/2范数问题转化为一系列具有迭代更新权的加权L1同伦最小化问题来求解。为了评估该算法的性能，在一个异质小鼠模型上进行了仿真，并将其与三种典型的稀疏重建算法进行了比较，包括加权内点算法、L1同伦算法和分段正交匹配追踪算法。仿真结果表明，该方法在不同的噪声水平下都能得到稳定的重建结果。定量比较结果表明，该算法在定位精度、多源分辨率和图像质量等方面均优于同类算法。
Bioluminescence tomography (BLT) is an important noninvasive optical molecular imaging modality in preclinical research. To improve the image quality, reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem. The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L1-regularized methods have been investigated due to the sparsity-inducing properties of L1 norm. In this paper, we present a reconstruction method based on L1/2 regularization to enhance sparsity of BLT solution and solve the nonconvex L1/2 norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights. To assess the performance of the proposed reconstruction algorithm, simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms, including the weighted interior-point, L1 homotopy, and the Stagewise Orthogonal Matching Pursuit algorithm. Simulation results show that the proposed method yield stable reconstruction results under different noise levels. Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy, multiple-source resolving and image quality.