Robust transformed l1 metric for fluorescence molecular tomography

Sparse-Laplace hybrid graph manifold method for fluorescence molecular tomography

Objective.Fluorescence molecular tomography (FMT) holds promise for early tumor detection by mapping fluorescent agents in three dimensions non-invasively with low cost. However, since ill-posedness and ill-condition due to strong scattering effects in biotissues and limited measurable data, current FMT reconstruction is still up against unsatisfactory accuracy, including location prediction and morphological preservation.Approach.To strike the above challenges, we propose a novel Sparse-Laplace hybrid graph manifold (SLHGM) model. This model integrates a hybrid Laplace norm-based graph manifold learning term, facilitating a trade-off between sparsity and preservation of morphological features. To address the non-convexity of the hybrid objective function, a fixed-point equation is designed, which employs two successive resolvent operators and a forward operator to find a converged solution.Main results.Through numerical simulations andin vivoexperiments, we demonstrate that the SLHGM model achieves an improved performance in providing accurate spatial localization while preserving morphological details.Significance.Our findings suggest that the SLHGM model has the potential to advance the application of FMT in biological research, not only in simulation but also inin vivostudies.

Dynamic fluorescence molecular tomography metabolic parameters solution based on problem decomposition and prior refactor

Dynamic fluorescence molecular tomography (DFMT), as a noninvasive optical imaging method, can quantify metabolic parameters of living animal organs and assist in the diagnosis of metabolic diseases. However, existing DFMT methods do not have a high capacity to reconstruct abnormal metabolic regions, and require additional prior information and complicated solution methods. This paper introduces a problem decomposition and prior refactor (PDPR) method. The PDPR decomposes the metabolic parameters into two kinds of problems depending on their temporal coupling, which are solved using regularization and parameter fitting. Moreover, PDPR introduces the idea of divide-and-conquer to refactor prior information to ensure discrimination between metabolic abnormal regions and normal tissues. Experimental results show that PDPR is capable of separating abnormal metabolic regions of the liver and has the potential to quantify metabolic parameters and diagnose liver metabolic diseases in small animals.