Bioluminescence tomography (BLT) has important applications in the in vivo visualization of a pathological process for preclinical studies. However, the reconstruction of BLT is severely ill-posed. To recover the bioluminescence source stably and efficiently, we use a log-sum regularization term in the objective function and utilize a hybrid optimization algorithm for solving the nonconvex regularized problems (HONOR). The hybrid optimization scheme of HONOR merges second-order information and first-order information to reconstruction by choosing either the quasi-Newton (QN) or gradient descent step at each iteration. The QN step uses the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm (L-BFGS) to acquire second-order information. Simulations and in vivo experiments based on multispectral measurements demonstrated the remarkable performance of the proposed hybrid method in the sparse reconstruction of BLT.

中文翻译：

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具有对数和正则化的多光谱生物发光层析成像的混合重建方法。

生物发光层析成像（BLT）在临床前研究病理过程的体内可视化中具有重要的应用。但是，BLT的重建存在严重不适。为了稳定有效地恢复生物发光源，我们在目标函数中使用对数和正则化项，并利用混合优化算法来解决非凸正则化问题（HONOR）。HONOR的混合优化方案通过在每次迭代中选择准牛顿（QN）或梯度下降步骤将二阶信息和一阶信息合并以进行重构。QN步骤使用有限内存的Broyden-Fletcher-Goldfarb-Shanno算法（L-BFGS）来获取二阶信息。模拟和体内 基于多光谱测量的实验证明了该混合方法在BLT稀疏重建中的卓越性能。