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.

为克服生物发光断层成像(BLT)的不适定性,获得稳定的光源重建结果,本文提出了一种基于连续化原对偶有效集(PDASC)的多光谱BLT重建算法,该算法将原对偶有效集算法(PDAS)与连续化技术相结合,可以自动调节正则化参数,从而获得全局最优解。多组数字鼠仿真实验验证了该算法的有效性和稳定性,且与原对偶有效集算法、硬阈值追踪法(HTP)相比,所提PDASC重建算法在不同光源设置下的各量化指标均表现更优,在体小鼠实验结果进一步证明了该算法在实际应用中的潜力。

提出将百分位半阈值匹配追踪法(PHTPA)应用于生物发光断层成像(BLT)这一光学分子成像模态领域。将BLT光源重建为一个L1/2范数正则化问题,在迭代半阈值算法(HTA)的基础上,结合子空间跟踪和百分位阈值法对其求解。在数字鼠模型上设计多组仿真实验,对改进的半阈值算法进行有效性和收敛性的评估。仿真结果表明,与原有的HTA和迭代重赋权算法相比,PHTPA在不同光源设置下都能得到更为准确的重建结果。

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 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.

生物发光断层成像(BLT)是利用生物体表面光强信息,重建出荧光光源在生物体内三维分布的一种新兴光学分子影像技术。由于生物体表面的测量信息有限,并且生物体内组织结构复杂,BLT的光源重建有着严重的病态性。为提高光源重建质量,提出了一种结合可行域收缩策略的多级自适应有限元光源重建方法。为了评估该方法的光源定位能力和能量密度定量能力,在数字鼠模型上分别设计了单光源和双光源实验。结果表明,本文方法可以显著提高光源的定位精度和能量密度。

生物发光断层成像(BLT)是一种低成本、高灵敏、具有巨大潜力的光学分子成像模态，高效稳定的重建算法是将其推向实用的关键。为克服BLT重建的高不适定性，提出了基于非凸L1-2正则化的重建方法，采用凸差分算法来解决非凸泛函最小化问题，在每一步迭代中采用带自适应惩罚项的交替方向乘子法高效求解。为评估该方法的有效性和稳健性，设计了单光源和双光源数字鼠仿体实验，并与3个典型的重建算法进行对比，仿真实验结果表明，所提L1-2正则化方法在不同光源设置下都得到了最准确的光源定位。

Bioluminescence tomography (BLT) is a novel optical molecular imaging technique that advanced the conventional planar bioluminescence imaging (BLI) into a quantifiable three-dimensional (3D) approach in preclinical living animal studies in oncology. In order to solve the inverse problem and reconstruct tumor lesions inside animal body accurately, the prior structural information is commonly obtained from X-ray computed tomography (CT). This strategy requires a complicated hybrid imaging system, extensive post imaging analysis and involvement of ionizing radiation. Moreover, the overall robustness highly depends on the fusion accuracy between the optical and structural information. Here, we present a pure optical bioluminescence tomographic (POBT) system and a novel BLT workflow based on multi-view projection acquisition and 3D surface reconstruction. This method can reconstruct the 3D surface of an imaging subject based on a sparse set of planar white-light and bioluminescent images, so that the prior structural information can be offered for 3D tumor lesion reconstruction without the involvement of CT. The performance of this novel technique was evaluated through the comparison with a conventional dual-modality tomographic (DMT) system and a commercialized optical imaging system (IVIS Spectrum) using three breast cancer xenografts. The results revealed that the new technique offered comparable in vivo tomographic accuracy with the DMT system (P > 0:05) in much shorter data analysis time. It also offered significantly better accuracy comparing with the IVIS system (P < 0:04) without sacrificing too much time.

生物发光断层成像(BLT)是一种高灵敏度、高特异性的光学分子影像技术，可根据探测到的生物体表光强来重建发光光源在生物体内的三维分布。由于生物体表面测得的光强信息有限，光源重建面临巨大的挑战。为了在有限的测量条件下获得更精确的重建光源，结合BLT中光源稀疏分布的特征，将重建问题转化为L1范数优化问题，并采用迭代支撑检测(ISD)算法实现快速重建，该算法交替执行支撑集检测和信号重构两个模块，直至重建精度达到要求。为了评估ISD算法的光源定位能力，设计数字鼠仿真实验，并与三种典型的稀疏重建算法比较。仿真结果表明ISD算法对于单光源和双光源目标均可以实现准确的重建。