Bioluminescence tomography (BLT) is a promising imaging modality that can provide noninvasive three-dimensional visualization information on tumor distribution. In BLT reconstruction, the widely used methods based on regularization or greedy strategy face problems such as over-sparsity, over-smoothing, spatial discontinuity, poor robustness, and poor multi-target resolution. To deal with these problems, combining the advantages of the greedy strategies as well as regularization methods, we propose a hybrid reconstruction framework for model-based multispectral BLT using the support set of a greedy strategy as a feasible region and the Alpha-divergence to combine the weighted solutions obtained by L1-norm and L2-norm regularization methods. In numerical simulations with digital mouse and in vivo experiments, the results show that the proposed framework has better localization accuracy, spatial resolution, and multi-target resolution.Bioluminescence tomography (BLT) is a promising imaging modality that can provide noninvasive three-dimensional visualization information on tumor distribution. In BLT reconstruction, the widely used methods based on regularization or greedy strategy face problems such as over-sparsity, over-smoothing, spatial discontinuity, poor robustness, and poor multi-target resolution. To deal with these problems, combining the advantages of the greedy strategies as well as regularization methods, we propose a hybrid reconstruction framework for model-based multispectral BLT using the support set of a greedy strategy as a feasible region and the Alpha-divergence to combine the weighted solutions obtained by L1-norm and L2-norm regularization methods. In numerical simulations with digital mouse and in vivo experiments, the results show that the proposed framework has better localization accuracy, spatial resolution, and multi-target resolution.

Fluorescence molecular tomography (FMT) allows the detection and quantification of various biological processes in small animals in vivo, which expands the horizons of pre-clinical research and drug development. Efficient three-dimensional (3D) reconstruction algorithm is the key to accurate localization and quantification of fluorescent target in FMT. In this paper, 3D reconstruction of FMT is regarded as a sparse signal recovery problem and the compressive sampling matching pursuit (CoSaMP) algorithm is adopted to obtain greedy recovery of fluorescent signals. Moreover, to reduce the modeling error, the simplified spherical harmonics approximation to the radiative transfer equation (RTE), more specifically SP₃, is utilized to describe light propagation in biological tissues. The performance of the proposed reconstruction method is thoroughly evaluated by simulations on a 3D digital mouse model by comparing it with three representative greedy methods including orthogonal matching pursuit (OMP), stagewise OMP (StOMP), and regularized OMP (ROMP). The CoSaMP combined with SP₃ shows an improvement in reconstruction accuracy and exhibits distinct advantages over the comparative algorithms in multiple targets resolving. Stability analysis suggests that CoSaMP is robust to noise and performs stably with reduction of measurements. The feasibility and reconstruction accuracy of the proposed method are further validated by phantom experimental data.

作为辐射传输方程的高阶近似, 简化球谐近似模型成为近年光学分子成像研究的重点, 但计算效率低限制了它的广泛应用, 为此提出一种基于图形处理器的并行加速策略, 采用NVIDIA 公司推出的统一计算设备架构,对求解过程中耗时最多的两个模块——有限元刚度矩阵的生成和线性方程组的求解进行基于图形处理器的并行加速; 根据统一计算设备架构的特点, 进行计算任务的分配、存储器的合理使用以及数据的预处理三方面的优化; 仿体及数字鼠仿真实验对比刚度矩阵生成时间以及平均迭代时间, 以评价所提出方法的加速效果。实验结果表明, 该方法可使求解速度提高30倍左右, 展示了该方法在光学分子成像中的优势及潜力。

为降低荧光分子断层成像(FMT)重建的病态性，受压缩感知理论启发，提出一种结合自适应可行区域迭代收缩策略和分段正交匹配追踪算法的重建方法。通过选取高荧光产额节点所在区域，迭代缩小可行区域，使目标函数有一个全局最优解。数字鼠模型上单目标及双目标重建结果表明，贪婪算法结合区域收缩策略不仅可以显著提高荧光目标的定位精度和荧光产额的定量分布，还可以降低算法对参数选取的依赖。物理仿体实验进一步验证了该方法在实际FMT 应用中的可行性和稳定性。

为提高荧光分子断层成像(FMT)的重建精度，将非凸LP (0 < P < 1) 正则化引入到FMT 重建中。为有效求解包含非凸惩罚项的优化问题，结合加权同伦算法(WHA)提出了快速迭代重建算法，将LP 正则化问题转化为一系列加权的L1 正则化问题求解。异质仿体上的仿真实验表明，LP (0医用光学 非凸正则化 加权同伦算法 荧光分子断层成像 重建算法 

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光学学报

2015, 35(7): 0717001