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This study proposed a Sparse‐Graph Manifold Learning (SGML) method to balance the sparseness and morphology preserving for bioluminescence tomography reconstruction. It inherits the benefits of non‐convex sparsity constraint and dynamic Laplacian graph model. The results of numerical simulations and in vivo experiments demonstrate that the proposed method yields accurate and robust results in terms...
In preclinical researches, bioluminescence tomography (BLT) has widely been used for tumor imaging and monitoring, imaged‐guided tumor therapy, and so forth. For these biological applications, both tumor spatial location and morphology analysis are the leading problems needed to be taken into account. However, most existing BLT reconstruction methods were proposed for some specific applications with...
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