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Adaptive regularized method based on homotopy for sparse fluorescence tomography

机译：基于同态的稀疏荧光层析成像自适应正则化方法

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摘要

Determining an appropriate regularization parameter is often challenging work because it has a narrow range and varies with problems, which is likely to lead to large reconstruction errors. In this contribution, an adaptive regularized method based on homotopy is presented for sparse fluorescence tomography reconstruction. Due to the adaptive regularization strategy, the proposed method is always able to reconstruct sources accurately independent of the estimation of the regularization parameter. Moreover, the proposed method is about two orders of magnitude faster than the two contrasting methods. Numerical and in vivo mouse experiments have been employed to validate the robustness and efficiency of the proposed method.
机译：确定合适的正则化参数通常是一项艰巨的工作，因为它的范围狭窄且随问题而变化，这很可能会导致较大的重建误差。在这一贡献中，提出了一种基于同态的自适应正则化方法，用于稀疏荧光层析成像重建。由于自适应正则化策略，所提出的方法始终能够独立于正则化参数的估计而准确地重构源。而且，所提出的方法比两种对比方法快大约两个数量级。数值和体内小鼠实验已被用来验证该方法的鲁棒性和效率。

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