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Nonlinear greedy sparsity-constrained algorithm for direct reconstruction of fluorescence molecular lifetime tomography

机译:非线性贪婪稀疏约束算法直接重建荧光分子寿命层析成像

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摘要

In order to improve the spatial resolution of time-domain (TD) fluorescence molecular lifetime tomography (FMLT), an accelerated nonlinear orthogonal matching pursuit (ANOMP) algorithm is proposed. As a kind of nonlinear greedy sparsity-constrained methods, ANOMP can find an approximate solution of L0 minimization problem. ANOMP consists of two parts, i.e., the outer iterations and the inner iterations. Each outer iteration selects multiple elements to expand the support set of the inverse lifetime based on the gradients of a mismatch error. The inner iterations obtain an intermediate estimate based on the support set estimated in the outer iterations. The stopping criterion for the outer iterations is based on the stability of the maximum reconstructed values and is robust for problems with targets at different edge-to-edge distances (EEDs). Phantom experiments with two fluorophores at different EEDs and in vivo mouse experiments demonstrate that ANOMP can provide high quantification accuracy, even if the EED is relatively small, and high resolution.
机译:为了提高时域荧光分子寿命层析成像(FMLT)的空间分辨率,提出了一种加速的非线性正交匹配追踪(ANOMP)算法。作为一种非线性贪婪稀疏约束方法,ANOMP可以找到L0最小化问题的近似解。 ANOMP由两部分组成,即外部迭代和内部迭代。每次外部迭代都会根据失配误差的梯度选择多个元素来扩展逆寿命的支持集。内部迭代基于外部迭代中估计的支持集获得中间估计。外部迭代的停止标准基于最大重构值的稳定性,并且对于目标处于不同边到边距离(EED)的问题具有鲁棒性。在不同的EED处使用两个荧光团进行的幻影实验和体内小鼠实验证明,即使EED相对较小,ANOMP仍可以提供高定量精度和高分辨率。

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